{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# ABU量化系统使用文档 \n",
    "\n",
    "<center>\n",
    "        <img src=\"./image/abu_logo.png\" alt=\"\" style=\"vertical-align:middle;padding:10px 20px;\"><font size=\"6\" color=\"black\"><b>第7节 寻找策略最优参数和评分</b></font>\n",
    "</center>\n",
    "\n",
    "-----------------\n",
    "\n",
    "作者: 阿布\n",
    "\n",
    "阿布量化版权所有 未经允许 禁止转载\n",
    "\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
    "\n",
    "[本节ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)\n",
    "\n",
    "上一节主要讲解了如何使用abupy中度量模块对回测的结果进行度量，本节将主要讲解使用grid search模块对策略参数寻找最优。\n",
    "\n",
    "最优参数的意思是比如上一节使用的卖出因子组合使用:\n",
    "\n",
    "    sell_factors = [\n",
    "        {'stop_loss_n': 1.0, 'stop_win_n': 3.0,\n",
    "         'class': AbuFactorAtrNStop},\n",
    "        {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},\n",
    "        {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}\n",
    "    ]\n",
    "    \n",
    "AbuFactorAtrNStop止盈止损策略的止盈阀值是3.0，止损阀值是1.0，那么止盈参数设置成2.0或者止损阀值设置为0.5是不是回测交易效果更好呢，\n",
    "AbuFactorPreAtrNStop风险控制止损pre_atr_n参数设置的是1.5，如果设置为1.0回测结果将如何，grid search模块对策略寻找最优参数就是要在给定的参数组合范畴内，寻找到策略参数最佳的设置。\n",
    "\n",
    "\n",
    "首先导入abupy中本节使用的模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "enable example env will only read RomDataBu/df_kl.h5\n"
     ]
    }
   ],
   "source": [
    "# 基础库导入\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import ipywidgets\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy\n",
    "\n",
    "# 使用沙盒数据，目的是和书中一样的数据环境\n",
    "abupy.env.enable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 参数取值范围\n",
    "\n",
    "\n",
    "Grid Search实际上是蒙特卡洛方法的一种实现子集，它首先固定了几组参数取值范围，把无限个解问题先缩小到有限个解的问题，然后对排列组合的各个参数组合迭代进行运算，得到最优结果。\n",
    "\n",
    "既然是在给定参数范围内寻找最优，所以首先要给定一个合理的参数范围，下面首先寻找AbuFactorAtrNStop的参数组合如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AbuFactorAtrNStop止盈参数stop_win_n设置范围:[ 2.   2.5  3.   3.5  4. ]\n",
      "AbuFactorAtrNStop止损参数stop_loss_n设置范围:[ 0.5  1.   1.5]\n"
     ]
    }
   ],
   "source": [
    "from abupy import AbuFactorAtrNStop, AbuFactorPreAtrNStop, AbuFactorCloseAtrNStop, AbuFactorBuyBreak\n",
    "from abupy import abu, ABuFileUtil, ABuGridHelper, GridSearch, AbuBlockProgress, ABuProgress\n",
    "\n",
    "stop_win_range = np.arange(2.0, 4.5, 0.5)\n",
    "stop_loss_range = np.arange(0.5, 2, 0.5)\n",
    "\n",
    "sell_atr_nstop_factor_grid = {\n",
    "              'class': [AbuFactorAtrNStop],\n",
    "              'stop_loss_n'   : stop_loss_range,\n",
    "              'stop_win_n'   : stop_win_range\n",
    "         }\n",
    "\n",
    "print('AbuFactorAtrNStop止盈参数stop_win_n设置范围:{}'.format(stop_win_range))\n",
    "print('AbuFactorAtrNStop止损参数stop_loss_n设置范围:{}'.format(stop_loss_range))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之前小节构造因子对象的序列使用字典形式装载参数和class，如下所示\n",
    "\n",
    "    {'stop_loss_n': 1.0, 'stop_win_n': 3.0,\n",
    "     'class': AbuFactorAtrNStop},\n",
    "\n",
    "上面sell_atr_nstop_factor_grid使用类似的方法构造字典对象，区别就是所有字典中的元素都变成可迭代的序列了。\n",
    "   \n",
    "\n",
    "使用类似的方式设置AbuFactorPreAtrNStop与AbuFactorCloseAtrNStop参数设置范围，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "暴跌保护止损参数pre_atr_n设置范围:[ 1.   1.5  2.   2.5  3. ]\n",
      "盈利保护止盈参数close_atr_n设置范围:[ 1.   1.5  2.   2.5  3.   3.5]\n"
     ]
    }
   ],
   "source": [
    "close_atr_range = np.arange(1.0, 4.0, 0.5)\n",
    "pre_atr_range = np.arange(1.0, 3.5, 0.5)\n",
    "\n",
    "sell_atr_pre_factor_grid = {\n",
    "              'class': [AbuFactorPreAtrNStop],\n",
    "              'pre_atr_n' : pre_atr_range\n",
    "         }\n",
    "\n",
    "sell_atr_close_factor_grid = {\n",
    "              'class': [AbuFactorCloseAtrNStop],\n",
    "              'close_atr_n' : close_atr_range\n",
    "         }\n",
    "\n",
    "print('暴跌保护止损参数pre_atr_n设置范围:{}'.format(pre_atr_range))\n",
    "print('盈利保护止盈参数close_atr_n设置范围:{}'.format(close_atr_range))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在参数参数设置范围确定了的前提下即可以开始对参数进行组合，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "卖出因子参数共有477种组合方式\n",
      "卖出因子组合0: 形式为[{'class': <class 'abupy.FactorSellBu.ABuFactorAtrNStop.AbuFactorAtrNStop'>, 'stop_loss_n': 0.5, 'stop_win_n': 2.0}, {'class': <class 'abupy.FactorSellBu.ABuFactorPreAtrNStop.AbuFactorPreAtrNStop'>, 'pre_atr_n': 1.0}, {'class': <class 'abupy.FactorSellBu.ABuFactorCloseAtrNStop.AbuFactorCloseAtrNStop'>, 'close_atr_n': 1.0}]\n"
     ]
    }
   ],
   "source": [
    "sell_factors_product = ABuGridHelper.gen_factor_grid(\n",
    "    ABuGridHelper.K_GEN_FACTOR_PARAMS_SELL,\n",
    "    [sell_atr_nstop_factor_grid, sell_atr_pre_factor_grid, sell_atr_close_factor_grid], need_empty_sell=True)\n",
    "\n",
    "print('卖出因子参数共有{}种组合方式'.format(len(sell_factors_product)))\n",
    "print('卖出因子组合0: 形式为{}'.format(sell_factors_product[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述代码使用ABuGridHelper.gen_factor_grid对参数进行组合，结果共组合出477种包括不同参数的组合，不同因子的组合，也有完全不使用任何因子的组合。\n",
    "\n",
    "相似方式使用ABuGridHelper生成不同买入参数的因子排列组合, 这里只使用42日、60日作为备选参数。读者可使用类似bk_days = np.arange(20, 130, 10)方式生成更多的买入参数，更可以自己实现其它的买入策略与AbuFactorBuyBreak一同做grid，但是在之后阶段运行Grid Search速度就会越慢。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "买入因子参数共有3种组合方式\n",
      "买入因子组合形式为[[{'class': <class 'abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak'>, 'xd': 42}, {'class': <class 'abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak'>, 'xd': 60}], [{'class': <class 'abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak'>, 'xd': 42}], [{'class': <class 'abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak'>, 'xd': 60}]]\n"
     ]
    }
   ],
   "source": [
    "buy_bk_factor_grid1 = {\n",
    "    'class': [AbuFactorBuyBreak],\n",
    "    'xd': [42]\n",
    "}\n",
    "\n",
    "buy_bk_factor_grid2 = {\n",
    "    'class': [AbuFactorBuyBreak],\n",
    "    'xd': [60]\n",
    "}\n",
    "\n",
    "buy_factors_product = ABuGridHelper.gen_factor_grid(\n",
    "    ABuGridHelper.K_GEN_FACTOR_PARAMS_BUY, [buy_bk_factor_grid1, buy_bk_factor_grid2])\n",
    "\n",
    "print('买入因子参数共有{}种组合方式'.format(len(buy_factors_product)))\n",
    "print('买入因子组合形式为{}'.format(buy_factors_product))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于只选用42d、60d为参数，所以只有三种排列组合，也就是分别为：\n",
    "\n",
    "* 只使用42d突破因子\n",
    "* 只使用60d突破因子\n",
    "* 同时使用42d、60d突破因子\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "买入因子有477种组合，卖出因子有3种组合，两者再组合将有477 x 3 = 1431中因子组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "组合因子参数数量1431\n"
     ]
    }
   ],
   "source": [
    "print('组合因子参数数量{}'.format(len(buy_factors_product) * len(sell_factors_product) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Grid Search寻找最优参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面代码使用abupy中的GridSearch类进行最优参数寻找，从GridSearch类参数可以看到除了buy_factors_product、sell_factors_product外，还有stock_pickers_product（选股因子排列组合）本例没有用到，读者可自行尝试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "read_cash = 1000000\n",
    "choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG',\n",
    "                  'usTSLA', 'usWUBA', 'usVIPS']\n",
    "grid_search = GridSearch(read_cash, choice_symbols,\n",
    "                         buy_factors_product=buy_factors_product,\n",
    "                         sell_factors_product=sell_factors_product)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面开始通过fit函数开始寻找最优，第一次运行select：run gird search，然后点击run select，如果已经运行过可select：load score cache直接从缓存数据读取\n",
    "\n",
    "备注：如果第一次运行选择run gird search下面的运行耗时大约1小时多，建议电脑空闲时运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43102219e18c4aa8b3e4dbbb2f68f33f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = None\n",
    "score_tuple_array = None\n",
    "\n",
    "def run_grid_search():\n",
    "    global scores, score_tuple_array\n",
    "    # 运行GridSearch n_jobs=-1启动cpu个数的进程数\n",
    "    scores, score_tuple_array = grid_search.fit(n_jobs=-1)\n",
    "    # 运行完成输出的score_tuple_array可以使用dump_pickle保存在本地，以方便之后使用\n",
    "    ABuFileUtil.dump_pickle(score_tuple_array, '../gen/score_tuple_array')\n",
    "\n",
    "def load_score_cache():\n",
    "    \"\"\"有本地数据score_tuple_array后，即可以从本地缓存读取score_tuple_array\"\"\"\n",
    "    global scores, score_tuple_array\n",
    "    \n",
    "    with AbuBlockProgress('load score cache'):\n",
    "        score_tuple_array = ABuFileUtil.load_pickle('../gen/score_tuple_array')\n",
    "        if not hasattr(grid_search, 'best_score_tuple_grid'):\n",
    "            # load_pickle的grid_search没有赋予best_score_tuple_grid，这里补上\n",
    "            from abupy import make_scorer, WrsmScorer\n",
    "            scores = make_scorer(score_tuple_array, WrsmScorer)\n",
    "            grid_search.best_score_tuple_grid = score_tuple_array[scores.index[-1]]\n",
    "        print('load complete!')\n",
    "        \n",
    "def select(select):\n",
    "    if select == 'run gird search':\n",
    "        run_grid_search()\n",
    "    else: # load score cache\n",
    "        load_score_cache()\n",
    "\n",
    "_ = ipywidgets.interact_manual(select, select=['run gird search', 'load score cache'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面使用type查看返回的score_tuple_array类型为list，score_tuple_array中的元素类型为AbuScoreTuple："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(list, abupy.MetricsBu.ABuMetricsScore.AbuScoreTuple)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(score_tuple_array), type(score_tuple_array[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面说过组合数量477 x 3 = 1431中因子组合：\n",
    "\n",
    "最终的评分结果也应该scores是有1431种，下面开始验证："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终评分结果数量1431\n"
     ]
    }
   ],
   "source": [
    "print('最终评分结果数量{}'.format(len(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "grid_search中保存了得到分数最高的对象best_score_tuple_grid，可以利用它直接用AbuMetricsBase可视化最优参数结果，如图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:38\n",
      "买入后尚未卖出的交易数量:2\n",
      "胜率:60.5263%\n",
      "平均获利期望:13.4462%\n",
      "平均亏损期望:-6.0647%\n",
      "盈亏比:3.2382\n",
      "策略收益: 31.5812%\n",
      "基准收益: 15.0841%\n",
      "策略年化收益: 15.7906%\n",
      "基准年化收益: 7.5420%\n",
      "策略买入成交比例:100.0000%\n",
      "策略资金利用率比例:18.0478%\n",
      "策略共执行504个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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xErfddjPXXPMTli17gSuv/DaGMYOEhIQBP3vKlCJ+8IPvYrfbycrKYubM2RGvX0REZDg0\nupsASItNxWQycd6Us7BZYnh5z3+5Z939tHhamZo6mauO/w6rK9fzxr63+bBidfh+s8lMIBDo9QeC\nEn0+v4+djXsw0oq6tVGDKxh40mNTe9wTHtKmOTyjmmmkr0RSU9MysgvsxUcfrSA1NY0ZM2axevUq\n/va3v/LHP/55WN6VlZVETU3LsDxbBk7tEH1qg+hTG4wMaofB29mwm9+vf4izJpzKeVPOCh9fXvY+\nL+x8GYAvGRdzyrhFQHCBg/XVm/nwwMfsbtpLh9/L1NTJTE+fxtcWXqB2iLJD/y68v/8j/uH4F1cd\n/x1mpE8LH//wwGqe3v48l06/hCX5C7s9w+V1ce17tzA7YzpXHveto1b7WHE0/z3Kykrq8ycN6uEZ\nBnl54/jNb27DYrHg9/u5+urrol2SiIiIHEZjaIGClNjuQ5tOLTwFuzWezbXbWJBzfPi42WRmfs5x\nzM85jv+WvMXLe15nZ+Medjbu4WsLLziqtcvhHWitAqDWWdfteIOrAYD0uJ49PLGWWMwms4a0jXIK\nPMNg4sRJPPTQX6NdhoiIiByBziFtqbHJPc4tylvAorwFfd6bn5Db7etWd1tki5Mhq3UFg05LaJPR\nTvWhOTxpvQQek8lEvDVOQ9pGOS1LLSIiIgLhJahTY3tOXj+c/MTugWdf0/6I1CSRU+esB6DF0z2M\nds7hSetlDg8E5/FolbbRTYFHREREhIM9PIcOaRuI9Lg05mTODPf07Gs6ENHaZGj8AT91oaFrLZ7u\nc0oaXI0k2hKIsdh6vddutdPkaeFXq34X/jMio4sCj4iIiAjBOTxmk5mkmIGvTtrJbDJzxdxv8o2Z\nXwIUeEaaZk8LXn9w38SWjoND2gKBAPXuxl7n73SKD+3Fc6CtkvXVm4e3UBkWCjwiIiJyzNjTVMpv\nPv491e01Pc41uptIiUnGbBr8t0c5CdmYTWbKGjWkLZJcXjdbarcx2NWFa0PD2aD7kLZ6VyNev5e0\nuLQ+7/X4D2490rlnj4wuCjwiIiIyZgQCAR4vfobndvy71/Nv7XuX8tYDvL9/Zbfj/oCfZk9LrwsW\nHAmb2Up2fCb7mg8M+ptz6emFnS/x4Ka/Uly3/fAXE1yYYENFMcV1DorrHGyp3dbl3MEhbR9XrgNg\nRvrUPp/V4Do4jO1Aa+WRli4jgFZpExERkTFjc+1WVletx4SJMyd8mpQuAaa9w8mW0DfMa6s2clHR\nOeHenHpXA76Aj4z49CHXkJ+YS2V1NQ3uRtL76TmQgWlyN4eDyeqq9czOnNHntY3uJkyYuG/DIxxo\n6xlObGYb7V4nXr8Xs8nMhxUfE2OJYUHOvD6f+dUZl/B22Qp2Nu5W4BmlFHhERERkTAgEAryy943g\nrwmwtnojpxaeEj6/vmYTXr+XOEssTZ5mdjXuYVpaEQD7Q9/IHrq89GDkJ+Syjk0caK1U4ImAd8s/\nxBvwYcLEpppiXF43cdbYHtftaNjFg5sex+f34Qv4mJMznckJk8LnE2x2tjXsZH31Jlo72tjfWkm9\nq4EleSeG5+n0Znr6VKanT+X+DY+ytd5BW0c7CTb7sHxWGR4a0iYiIiJjQo2zlv2tFUxJmYTZZGZN\n5YbwOa/fyxslb2MxWbiw6BwAdjTsCZ8/0FoBwLjEvCHX0blEdW89DHJkXF437+//iERbAp8uXIrH\n38Guxj09rvMH/Dy65Wl8fh/+gB+rycKVC7/GGRM/Hf7v5HEnkRyTBATn8XxwYBUAS8edNKBawu0a\n+rMio4cCj4iIiIwJ2+t3AbAwdx7T06ZS2lJGdXsNHb4OXtz1CrWuek4Zt4h5WXMAKG0uC9+7PxRO\nDt1PZzDyE4KhScOfhu6jitW0e518smAJhUnjAGjoZWnoZk8LrR1tzM2cyY/nX8kP511OZkLP4YnJ\nMYkA7G89wObarRQk5jM+qWBAteQl5ABQ2cuCF/0pad7H09uep8PXcUT3SeRoSJuIiIiMCY6GnQBM\nT5tKjNnG1noHL+3+L+WtB6hx1pERl8ZZEz9DYkwCmXHplDaXEQgEMJlMHGitJN4a1+fmk0ciIz6N\nWEuMeniGyOf38XbZ+9jMVj4xbgnlrcGlvptDG8R21bkKW2Z8BpNTJvb5zKRQ4Hm9dDn+gJ+T80/C\nZDINqJ4cexZAryv89ee+DY/g9LoYn1zAKeMWH9G9clC9q4F/7nwZAgHOnHgqE5ILB3yvAo+IiIiM\nev6AH0fDbjLi0smyZ5AUk4DNYWN9zWZMmPh04VLOnXRmeO7HhORC1lZvZFv9Dva3VlDjrGVi8vgB\nf/PbH7PJTEFKHqUN5fj8Pixmy5CfeSzaULOFOlcDp4xbTGJMQngBiuZDNg4FqAsHnv4XnZiZbmA1\nW6luryXGbGNh7vEDrid7kIHH6XUBUNNed0T3SXcfHVjNxpotAGxr2MnXZnwh3Fv735LlfD3rwj7v\n1ZA2ERERGfX2tZTj9DqZnh5chCDOGscZEz6FkVbETxb8kM9PPb/bRPeJoZ8O37/xUZbtfpVAIMAJ\n2XMjVs/4lHF4Az6qnbURe+axZmXFGoDwwhMpofk3Tb0EnlpnMExkxmf0+8y0uFQ+EeplmZ9zPPHW\n+AHXk2Czk2hLoOoIA0+iLbiRrXr8hmZ7Q3DI6qXTLwnN2XqKhzY/zsqKNbyy9/V+71UPj4iIiIx6\n2+tDw9nSp4WPnT3p9D6vn5s1ixUHVpFjz2Zu1izmZMwgMSYhYvUUpuQDwQnunXM/5MiUtx4gPS6N\nbHsmAPHWeKxmK83uXgKPa2A9PABnTzoNi8nCJwuWHHFN2fYsSpr34fV7sZoP/210IBDA7QtuXNp1\nCKUcGafXRUnzPiYlj2dJ/kKmpE7kme0vsLl2G5u77LHUFwUeERERGVZevxeX1x3RQHGo7fU7MWFi\nWuqUAV2fGZ/BLYuuH7Z6xncGnrYq5g/bW8auFk8rzZ4WZmcc3HPHZDKRHJPU65C2Wmc9ZpN5QHOw\n4q3xXFh09qDqyrZnsqephFpnPbkJ2Ye9vrWjjQ5/cLGCdq+TamdteC6QDNyuxj34A36M0AaxOfYs\nfjTve6ysXMu/d7/KcZmz+r1fQ9pERERkWC3b9So//+iOXr9RjQS3z8OeplIKkvKHNVQdifGpwRXF\ntFLb4HT+vh26THhn4AkEAuFjPr+PWmcdabGpwz5fqjOsDHR4WoOrEYBYSwwA7+//aHgKG8MCgQDL\n970P0CMAL85bwG9OvpkvGRf3+wz18IiIiMiw2t1UgsvnZmNNMaeMWxTx5+9q3Isv4GN62tSIP3uw\nUmKTSLQlaM+WQahur2Vt9Uag5zLhKTFJlAR8bKnbRlnLfnY3lrC3uRS3z3NU2n9a2hRMmPjXrv8w\nLXXKYQN2vTsYeM6a+BlW7F/J++UfUd1+7Mzriomx4vF4B3Tt3MyZLB23iFUVa6l3NfLZSZ8hEAiw\nsnItOxp3MzPDYFLK+B73DWSIoAKPiIiIDJtAIBBe1WpjzZZhCTzb63cAMD195AQek8lEfkIuOxp3\n4/Z5wj/hl8N7ePMTVLRVAb308IRWavvzpsfDx3Ls2UxJmcgnBjEn50hNTB7POZNO55W9b/Bo8dNc\nddy3++1Vqnc1AJAVn8mFRefw2JanKa7bPux1jkbFddspaz3Ayoo1eP1eUuNS2Fizmc2127CZrVw4\nZXDDEEGBR0RERIZRs6cFl88NgKNhF+0d7dht9oi+w9GwC5vZypR+9l+JhvzEYODZ0bCLVk8biTEJ\nzMmcGe2yRrQOvzccdqxmK9nxmd3OJ3RZVe3bs7/K1NTJ4b11jpYzJ57Kvpb9bKot5t+7X+Piqed2\nO+/1e9nRsJuNtcWs2L8SgPS4VCYkFzLnUzPxB/xHtd5oysxMpLa29bDXNbmbeXDjY+HfL4Cntj0H\nwLTUKXzJuIicAcyZ6osCj4iIiAwLl9fF7qYSAOIscbh8LrbUbefE3BMi9o5mTwv7WyuYnjYVm8UW\nsedGQn5CcDjWX4v/Hl6p60fzvse0tIEtrHAs6lxeekrKJC6eek6P3pPEULgx0ooiuoz4kTCbzHx9\n5he5a819vFX2HrkJOSzJXwjAf/b+j+X73sflC+69Y8JERlwaOfbgN+u2AazsNpbEWWOJtXgOe122\nPZNr5l/Jw5uexGwyk2PPZEvddi6ccjYn5p4w5JXtjq3fdRERETlq/rL5b2xvCC4X/YmCxbxR+jYb\narZENPA46oN7c4yk4WydOuefdIYdgI8r1ynw9KNzj5s5mTOYmNxzvsbJ+SdiM1tZGME/Q4MRb43j\ne3O+zl1r7+fp7c/zUcXHFCYV8G75ByTHJLEkfyFzM2cxOWWCNp4doOSYJK5b8INuC1JEagnvQQUe\nwzDMwAPAcYAb+I7D4djV5fzngBuAAPC0w+H4w+HuERERkbHD6/eGww7A3MxZbKopZmudI6JzWjr3\n3zFCG46OJF333zkpdz47GnazvnozX5x24YjrjYqWHQ27yLHnkBIb3FS0ui0YePpa8jnGEsPSYZgH\nNhg5Cdlcc8IV/GXzk+xt2seeplIgONSuKHVSlKsbvYZjn6LB9vBcCMQ5HI7FhmEsAu4BLgAwDMMC\n3AEsAFqBrYZhPA18oq97REREZGzZf8jqZDn2LI7Lms3rpcvZVufg+Ow5Q35HIBBge8NOEmx2ChLz\nh/y8SIuzxpERl0adq4Hp6VNJiU3mjdK3+Wvx35mVMT3a5UVFvC2e4zJnYTFb2FhTzMObnyDOEsf5\nU87ilHGLwj082aNkr5pxiXncuvinNLgaeaz4afISchV2RqDBBp6lwH8BHA7HSsMwFnSecDgcPsMw\nZjgcDq9hGNmABfD0d4+IiIiMLXub9wFwauEpTEmZiN0Wz/GhwLOhpnjIgae1o413ylbQ6G7ihOy5\nmE0jc2vBicnjaXI3Mz19KnMyZ7KnqYSNtcVsrC2OdmlRMytjOnMzZ7K87H1MmDCZTDy3YxmrKtZS\n2lKG2WQmMy492mUekbS4VK6d/4NolyF9GGzgSQaaunztMwzD6nA4vAChsHMxcD/wH6DtcPf0JS3N\njtWqsY/9ycpKinYJgtphJFAbRJ/aYGQYCe1QsTvYw3Pe7FMZlxycy5KZOZ3MrekU128jLT0eq2Vw\n34bsqd/Hre/9DpfXTUKMnYvmnEFWZvQ/86GyspK4cslXaXQ2MT412AN1a/Y1bKgoxu09/ETuseid\nko/YXLU9vDTzZyYv5YtzzuPJ9f9kxb7VABSm5JObkxqR942EvwvHupHQBoMNPM1A1+rNhwYXh8Px\nomEYy4DHga8P5J7eNDS0D7LEY0NWVhI1NcOzc7UMnNoh+tQG0ac2GBlGSjvsqikhzhKH1RVPjftg\nPXPSZ/J2+QpW7d7MtLTBzbt5cevruLxuzp10Jp8uXEpcIHZEfOauurZDPMnd6psUOwVio1VZdBXN\nmMrW7OA8LovZwqyM6XS0mPhy0SUsyjqJGmctk5InRKQ9R8rfhWPZ0WyD/oLVYAPPB8B5wHOh+Tib\nO08YhpEMvAyc4XA43IZhtAH+/u4RERGRsaXR3UxGfFqPoWadK5fVuRoH9Vy3z8OGmi1kxKVz1sRT\nh2WCswwfq9nK3KxZvZ6blDKeSSk9V2YTGarBBp5/AacbhvEhYAIuMwzjK0Ciw+F4OLRIwXuGYXQA\nm4CnCK7Y1u2eoZcvIiIiI43H14HL5yLJ1nNDyM5NIls8g/up74bqzXh8HhYWzlPYEZEBGVTgcTgc\nfuCKQw5v73L+YeDhXm499B4REREZYzrDTFJMzyEmyaFjLZ7D775+KK/fy6slb2IxWVict3BoRYrI\nMUMbj4qIiEhENYfCTHJM3z08zUfYw9PW0c7zO16i1lnHJwtOJjN+dK3iJSLRo8AjIiIiEXWwh6eX\nwGPrHNI2sB6eQCDAqsq1/GvXf2jtaGNcYh5nTzwtcsWKyJinwCMiIiIR1RLu4ek5pM1msRFvjRtw\n4Hlpz395o/RtYsw2LpxyNqcWnoLFrO0qRGTgFHhEREQkopr7CTwQ7PkZaODZWFNMnCWWm078MRnx\naRGrUUSOHSNzW2IREREZtVo6+h7SBpBkS6K1ow1/wN/vczw+D9XtNYxLzFfYEZFBUw+PiIiIRFRn\nD09vq7TrV2icAAAgAElEQVRBcDGDAAFaO9p69AL5/D5MJhMPb36SrXUOAgQYl5g37DWLyNilwCMi\nIiIR1exuwYSJRJu91/NJXZam7vB5SY1NxmK24PP7uH3VPeQm5LC5dmv4+gIFHhEZAgUeERERiaiW\njhYSbQl9Li7QuVz1O2Uf8GHFx1wy7QI+VXAye5v3Ue2spdpZ2+36cUkKPCIyeJrDIyIiIhHj9Xtp\ncDWRGpvc5zUpsSkAfFjxMQDrqzcBdOvV6SovITfCVYrIsUQ9PCIiIhIxJc1ldPg7mJw6sc9rTsie\nw4cHVrG3eR8AsZZYADbXbut23ZeNi7GYLMRaYoatXhEZ+9TDIyIiIhGzs2E3ANNSp/R5TZw1jh/O\nu5xvzPwSJkw0upuobq+lqr2aGenTsJmtpMQkcXL+SSzOX3i0SheRMUo9PCIiIhIxOxp2Y8JEUdrk\nfq+LtcRwYu4JvF6ynEZXE1vqgr0787Ln8MmCJdjMNkwm09EoWUTGOAUeERERiYgOXwd7mkvJT8wl\n0ZYwoHtSY1OobK9mXVVwHs/sjBmk9DP/R0TkSGlIm4iIiETE3uZ9eP1epqX1PZztUKlxKaF7Sxmf\nVKCwIyIRp8AjIiIiEbFjAPN3DpUaWrENYE7mjIjXJCKiwCMiIiIREZ6/k9r//J2uugeemcNRlogc\n4xR4REREZMg8Pg8lzfsoTMrHbosf8H1pocCTGptCQWL+cJUnIscwBR4REREZsj1NpfgCPqalFR3R\nfdn2LEyYOC5rtlZlE5FhoVXaREREZMjC83eOYMECgGx7Jj9Z8ENyE7KHoywREQUeERERGbodDbsx\nm8xMSZl4xPeOTy6IfEEiIiEa0iYiIiJD4vK6KW0pY0JSAXHWuGiXIyLSjQKPiIiIDEmDuxF/wE9+\nYl60SxER6UGBR0RERIbE6XUCYLcOfHU2EZGjRYFHREREhqS9IxR4jmA5ahGRo0WBR0RERIakXT08\nIjKCKfCIiIjIkBzs4bFHuRIRkZ4UeERERGRI2r3tgHp4RGRkUuARERGRIdGQNhEZyRR4REREZEi0\naIGIjGQKPCIiIjIkTq8LgHj18IjICGQdzE2GYZiBB4DjADfwHYfDsavL+S8DVwNeYDPwfYfD4TcM\nYx3QHLpsr8PhuGwoxYuIiEj0dc7hibfGRbkSEZGeBhV4gAuBOIfDsdgwjEXAPcAFAIZhxAO3A3Mc\nDke7YRjPAOcahvEGYHI4HJ+KQN0iIiISZf6An9WV66lzNhBvjcNs0sARERl5Bht4lgL/BXA4HCsN\nw1jQ5ZwbWOJwONq7vMNFsDfIHgo+VuAmh8OxcpDvFxERkShp72jnw4rVJMck8eS2ZwHIiEuLclUi\nIr0bbOBJBpq6fO0zDMPqcDi8DofDD1QBGIbxQyAR+B8wG7gbeASYCrxmGIbhcDi8g65eREREjqpA\nIMDftj3PptpiYiwx4eNaoU1ERqrBBp5mIKnL1+auwSU0x+dOYBrwOYfDETAMYwewy+FwBIAdhmHU\nAXlAWX8vSkuzY7VaBlnmsSErK+nwF8mwUztEn9og+tQGI8NwtsP7JR+zqbYYAI/PEz6eGG9X+x9C\nvx/RpzaIvpHQBoMNPB8A5wHPhebwbD7k/EMEh7ZdGOrxAfgWMAf4vmEY+QR7iSoO96KGhvbDXXJM\ny8pKoqamJdplHPPUDtGnNog+tcHIMJzt0Ohu4tG1/yDGEsOEpAJ2Nu4Jn6tqqVP7d6G/D9GnNoi+\no9kG/QWrwQaefwGnG4bxIWACLjMM4ysEh6+tAb4NvA8sNwwD4A/Ao8DjhmGsAALAtzScTUREZHQI\nBAI8s/0F2r1OvjjtIgqS8vj9uofIS8ihvPUA9a6GaJcoItKrQQWeUK/NFYcc3t7l130t0/KVwbxP\nREREomtt1Qa21G3HSCti6biTMJvM/P5Tv6LR3cRtK+/iC9MuinaJIiK9GmwPj4iIiBxDttXvBOBz\nU88LLz9tNplJj0vj95/6dTRLExHplxbMFxERkcNy+lwApMQmR7kSEZEjox4eERGRY4TH18Huxr34\nCa4nZDPbmJIyEYv58KuhOjucAMRb4oa1RhGRSFPgERGREWFPUwl5CTnEaz+XYfPcjmV8VLG627FL\np3+eJfkn9ri2yd1MgACpsSlAsIcnxhIzoHAkIjKSaEibiIhEXU17HfesfYA7V/+JWmddtMsZk8pa\n9rOyYg059mwumPJZzpxwKgA7Gnb3uNZRv4vbVt7Nn9b/JXzM2eFU746IjEoKPCIiEnUtHa0AVDtr\nuXvN/exrLo9yRWNLIBDghZ0vEyDAF6ZdwBkTPs15k88kwWpnT1Npt2tXV67n/o2P4vK5qGqvocPX\nAQR7eOJt6n0TkdFHQ9pERCTqPD4PAIWJ+ZS3VnDv+j/zyXFLsJr1v6kjlVAVQ1ubp9uxlo5Wdjbu\nYXbGdKanTwXAZDIxKWU8W+q20+RuITkmkf/te4d/736NeGsc2fZ8SpvLqHXVk2vPxul1kR2fFY2P\nJCIyJPo/iYjIEdjTVMLm2m2cP/ksTCZTtMsZM9yhwLMw9wTOik/n8eK/879970S3qDHGYrJwUdG5\n3Y5NSpnIlrrt7G0uZWfDbt4p/4DU2BR+cNy32VK3jdLmMmraa0mPS8Mf8BNv1ZA2ERl9FHhERI7A\nK3vewNGwixNzTyAvISfa5YwZHaHAE2OxcXzWbG5d/FNq2mujXNXolJpqp7GxvcfxlNgUsu2Z3Y5N\nTC4EYHXlOjbUbCHXns0P532X1NgUqttrAKhx1jHeWwCgwCMio5ICj4jIAHn93vB8hyZ3swJPBLn9\nocBjjgEgNTYlvDqYHJmsrCRqTC0DujY/MReA4rrtACzJPzH8+54VCkc1zjqc3uAePAo8IjIaadEC\nEZEB2teynw5/cAJ3k7s5ytWMLZ7QxPgYS0yUKzm2JNkSsVvj6fB7AchNyA6fy4zPAKCmvbZL4NGi\nBSIy+ijwiIgM0K6GPeFfK/BElic8pE2B52gymUzdQk6u/WCvZawlhpSYpFAPT2jTUfXwiMgopMAj\nIjJAOxsPBp5GT1MUKxl7PKGes1gFnqOuM+TEWGJIi+s+jDAzPoN6VwOtnjZAgUdERicFHhGRAfD5\nfexu2kuSLRFQD0+khXt4zLYoV3Ls6ezhybVnYzZ1/7YgPS6dAAEq2qoADWkTkdFJgUdEpB9N7haa\nPS2Utx7A7fMwN2sWFpNFgSfC3BrSFjXhwNNlaFunzPg0AMpbDwDq4RGR0UmrtImI9OOmD34JwEVF\n5wAwLXUyW+scNCrwRNTBRQvUw3O0TU2dzLzsuZycf1KPc+lx6cDBwBOnwCMio5ACj4jIAGyp3QZA\nUdpkUmNTKG0pwx/w9xgCJIPj8auHJ1piLDF8Z/ZXez2XERfs4WnxtAJg15A2ERmF9H9qEZE+dM4r\ngeCCBZnxGaTGppASm4w/4Keto+fmjjI4B+fwKPCMJBnx6d2+1pA2ERmNFHhERPpwaKCZmjoZgPS4\nVAD2NJUc7ZLGLI/PgwkTNrMGHowkaV02f+0M+yIio40Cj4hIH/oKPIvzFmI2mVm2+1W8oQ0bZWg8\n/g5sFhsmkynapUgXFrMl/OtFufM1hFNERiX9yyUi0od2b/fAUxQKPPmJuSzKXUB1ey17mkqjUdqY\n4/F5iNVwthGpc6nwk/IWRLkSEZHB0dgBEZE+tHbp4ZmUPIGM0BK9AHmJwc0a20M70MvQuH0eLVgw\nQl2/4IfUuerJtmdGuxQRkUFR4BER6UN7KPB8Y+aXODH3hG7n4iyxALi97qNe10jl8XXQ2tHW7Zjd\nGj+gYVAdvg7iYzUhfiTKT8wlPzE32mWIiAyaAo+ISB865/Ak2Ow9znXuR+L0ubod316/k3GJeSTF\nJA5/gYPkD/hp8bSREpsUsWc2uZu5dtnNuA4JgHMyZ3LF3G8e9n63Xz08IiIyPDSHR0SkD22hOTx2\na8/AE9tLD09ZywH+tOEvvFby1tEpcJCWl73Pzz78FTsadkfsmXub9+HyuilIzOf4rNkcnzWbBKsd\nR8Mu/AF/v/f6A368fm94roiIiEgkKfCIiPSh3x6eUOBx+Q4Gnq112wGoddYdheoGr6Q5uGnqP3e+\ndNgwMlAVrVUAnDf5TL475+t8d87XmZ4+FY/PQ4Orqd97O/fgiVUPj4iIDAMFHhGRPvQ/pC0UeLr0\n8Gxv2AVAo7v/b/Cjrbq9BoD9rRV8cODjiDyzsj0YePIScsLHchOyu53ri8ffAYBNgUdERIaBAo+I\nSB/aO9oxYep1d/nwogWhHh6Pz8Oexr1AcD7LSBUIBKhx1pEel0asJYZX9rxOe8fQV5qraKsi1hJD\nWmhTVoDcUPipaDtM4Ons4dGy1CIiMgwUeERE+tDW0Y7d1vsqY7HW7kPadjXuxRvwAdDa0UbHCN2Q\ntMnTjMfnYUJSAWdN/AytHW28VvLmkJ7pD/ipaq9hXHJut9+rzt6eyrbqfu93hwKPFi0QEZHhoMAj\nItKHNm87Cb0sWABd5vB4g6u0ba/fCUBKTDIwcnt5atprAciyZ/LpwlPIjEvnnfIPqDpMKOlPZVs1\nXr+XguS8bsez4zMxm8yHDTweX3BIW4xFixaIiEjkKfCIiPTC5/fR1tFOYkxCr+etZitWszXcw7O9\nYSdWs5UTsucCIzfwVDuDgSc7PhOb2coFRWfjD/h5p/yDw967tc7Bczv+3W0IXIunlYc2PQ7A9Kyi\nbtdbzBZy7FkcaKvod3GE1o5WILhnj4iISKQNah8ewzDMwAPAcYAb+I7D4djV5fyXgasBL7AZ+H7o\nVJ/3iIiMJHWuevwBP1nxfe8uH2eJxeV10+xpYX9rBdPTppIZnwGM3IULqkILFmTZg5/ruMxZpMQk\ns7pqPRcVndtnL0tbRzuPFz9Dm7cdR8Muvj/3MjLi01lZsYZaVz2nFp7CZyafTG1ta7f7ChLzqWir\notZZR7Y9q9dnl7XsB2BcYl6v50VERIZisBuPXgjEORyOxYZhLALuAS4AMAwjHrgdmONwONoNw3gG\nODf0rl7vEREZaapDQ7+y7f0HHrfPjaM++LOb6elTSY3tHNJ2+MCzvX4nj2z5W3i+j9lk5ivG51iY\nO2+o5fcqEAiwuXYrNrOVcYm5QLAXZlHeAl4vXc6tH92Bxdz7/xY8Pg9t3nbGJxWwr6Wcu9bex5Vz\nL6O4bjsmTJwx4dOYTKYe9xUmjWN11XrKWg70E3gOhK8VERGJtMEOaVsK/BfA4XCsBBZ0OecGljgc\njvbQ11bAdZh7RERGlPDQrz6+SYfgwgUurzs8f2d6+lRSYlMAaOxlSJvP78PpPTgc7K2y93B6XYxL\nyKMgMR+Pz8MHB1ZF8mN0s7uphOr2Wo7PmkN8l+Fjp4xbxLjEvD7DDgQXFJiZbnDt/O9zydQLaPW0\nce+6P7O7qYTxyQUkxST2el9hUj5wsBenN2Ut+0mOSSIlFBZFREQiabA9PMlA1x9f+gzDsDocDq/D\n4fADVQCGYfwQSAT+B3yhr3v6e1Famh2r1TLIMo8NWVlJ0S5BUDuMBJFsg5bS4D9XRv54stJ6f25S\nnJ0DrZU4mnaSFJvI8ZOm0eBsgrXgpA1bUgCb2UpCjJ0OXwe/fu8+ShrKuP/cX9HudbKtfgdTMybx\nq9N+AsCN/7uDPQ0lJKRYscfE0+Zpx2q2Emsd2uplXp+XfU0H+E/p6wB8dsYnu/1eZZHEvYW3DPh5\nl+ScxaScfP7w0aN0+P2cNP648PMObYP4lGmwHirdFSSm9hwu1+Zpp8HdyLy82fo7FEH6vRwZ1A7R\npzaIvpHQBoMNPM1A1+rNXYNLaI7PncA04HMOhyNgGEa/9/SloaH9cJcc07KykqipaYl2Gcc8tUP0\nRboN9tVXAGBxxff5XEvARoAADc4m5mcfR11tG/6AGavZSlljJVe+dBOxlhh+e8rPebz4GYqrdwDw\n/s511DnrCQQCLMw6Ifx8I3kqu+tLWbFzPXMyZ/CLlXfh9rn5/NTzWZgzr9chY4fj8XVwz9r7KW8N\nDhublz2XLFPukH+vJsRM4uoTruDDA6s5ITX4Gfpqg4y4dDZXOfjGi9f0+bycmGz9HYoQ/Xs0Mqgd\nok9tEH1Hsw36C1aDDTwfAOcBz4Xm42w+5PxDBIe2XRjq8RnIPSIiI0ZVew0pMcnEhfbb6U3n0tQA\nRnpwhTKzyUxmfAb7W4Mrk7V7nTyy+W9srC0mKz6DGmcdxbXbwyuTzc6YGX7GrMzpvFryJhtrthAg\nQL2rAYAntv6D1VXr+dK0i8mITzuiz/HS7tcobz2AkVbE8VmzWTpuUa/7Cg3G+KQCxhsFh73u/Mln\nsrpqQ5/nbWYri/IWRqQmERGRQw028PwLON0wjA8BE3CZYRhfITh8bQ3wbeB9YLlhGAB/6O2eIdYu\nIjIkXr+XnY17MNKKuoWADl8Hje4milIn9Xt/bJfAU5Q6OfzrnPhMKtuqwl9vrC0mx57Fj0/4Prev\nuoctddvwBXxkxmeQEnvwJ1ITkgrJis9gQ83m8ByiK+dexttlK9ha5+D2j+/h0wVLsduC829sZhtF\nqZPIT8jttfdnW/0O3i5fQY49myvmfjNqG3suyJ3HgmFaiEFERORwBhV4Qr02VxxyeHuXX/f148ND\n7xERiZplu17l7fIVXFR0DqeN/2T4eI2zjgCBfldoA7r1/mR3Wb4665D7kmyJfP+4b5EYk8DszBl8\nVLEagLmZs7pdZzKZWJJ3Iv/e8xqlzWVMSZnI7MwZzMqYzqrKtbyw82VeL13eo47U2BRmZRicMeFU\nMuPTAWjtaONvW5/DbDLzzVlfilrYERERibbB9vCIiIxqFW1VvLv/QwBe2/smJ+aeQHJMsLdlICu0\nAbSHVlyLt8Z162HpGn6unncFuQnZ4VXMzpxwajjwTE6Z0OOZJ+XN5419b5MWm8o3Zn4ZCAahRXkL\nmJ0xg73NpeFrWzva2V6/g211O/jgwMdsrClmduYMluYv4q1979LkaeaCyZ9lfNLhh52JiIiMVQo8\nInJMWle1EX/Az4z0aWyr38G7ZR9w3pSzAKgJ7cHT36ajAE6vC4DJKRO7He/s4bGYLExOmYDFbOly\nLoPzJp/J6yXLmZlh9HhmSmwyv1h8A3GW2G73ASTGJDAnc2a3Y4vzFuAP+FmxfxXP7/w3KyvWsLGm\nGKfXyZSUiZw24ZOIiIgcyxR4ROSY1Nk7c/ak09jbtI+VlWs5Z/IZmE1mqttrAMg5zJC2i6acg9lk\n5gvTuu+h3DkULsee1SO0AJw18TOcPv5TvZ4DSLDZj+izmE1mPlGwmAU5x/PK3td5t/xDzCYzXzIu\njtgCBSIiIqOV/k8oIsekzt6Z5JhkFuQeT6O7if+VvoPP76PaWYsJExnxGf0+I8uewXdmfzU8FK5T\nSkwyJ+XO55Rxi/u8t6+wMxR2WzznTT6LCUmFfHbiZ8hPzI34O0REREYb9fCIyDHJFQo8cdZYPl1w\nMmsqN/DSnv+yumo9je4m0uPSsJkH90+kyWTi6zO/GMlyByzeGsdPFv4wKu8WEREZidTDIyLHJKfP\nDUC8JY7chBxuWXQ9S/JOpLKtGqfXddgV2kRERGR0UA+PiByTXF4nMWZbeGhZSmwSl874PJ8oWMLb\nZe8zP+e4KFcoIiIikaDAIyLHJJfXTZw1rsfxwqT8qA1HExERkcjTkDYROSY5vS7iewk8IiIiMrYo\n8IjIMcnpcxFnUeAREREZ6xR4ROSY0+H34vV71cMjIiJyDFDgEZFjTtclqWVkqm5op9XZEe0yRERk\nDNCiBSIypjV7Wvhr8TPsbNhNYdI4rpv/A1ze4JLUvS1aINFV3+zihXf38FFxJfmZCdx62UKsFv1s\nTkREBk+BR0TGtIc3PcHe5n0k2hLY11LOhpotZNkzADSkbYR55cMSXvmwBI/XT3yshQO1bby5ppyz\nThof7dJERGQU04/NRGTM8vq9lDSXMSGpkGvnfx8TJt4qe+/gkDYtWjBi7Ktq4cX39hAfa+VbZ8/g\nju8tJj7WwvJ15dEuTURERjkFHhEZs+pdjQQIkJuQTbY9izmZMyltLmNr3Q5APTwjSWV9OwDnLJ7A\n0rl5JNljmJibTG2TC5fHG+XqRERkNFPgEZFRq72jneX73sPr7/0b4jpnPQCZ8ekAnFq4FIB3yz8A\ntGjBSFLT6AQgKzU+fCw/MwGAA7XtUalJRETGBgUeERm1ntvxEi/seoX/7P1fr+drXXUAZMYH5+wU\npU6mMGkcHn9w9a94a3yv98nR11vgGZcVDDz7a1ujUpOIiIwNCjwiMmpVt9cAsLNhd6/naw/p4TGZ\nTJxaeEr4fJxFPTwjRU1jcF5VZsrBYYbjQj08+2vaolKTiIiMDQo8IjJqxVhsADS4m3o93xl4MuIy\nwsdOyJ5LamwKoDk8I0lNo5PUxBhibJbwsXHhIW0KPDJw+6paqA31GIqIgAKPiIxida4GABrdTTQ6\nD4ae4rrt/HLVPWyo2QxAckxi+JzVbOX8yWeRl5BDbkL20S1YeuX1+alrdnUbzgZgj7ORlhRLWU0r\ngUAgStXJaOL3B/jt39fx0MvF0S5FREYQ7cMjIqOS1++lwdUY/npdRTHjYybwz50vsa56U/h4gs2O\nyWTqdu9JefM5KW/+UatV+lff7CIQoEfgAZicl8zaHTXUNvUMRCKHqm9x4XT7KKloocPrx+P14fb4\nSE9Wb67IsUyBR0RGpc4lp4tSJ1HSXMbTm/6F1+fF6XUxKXk8X5h2IZtqt5KfmBvtUuUwapt6zt/p\nNLUwlbU7athR1qjAM8rUN7vw+wNkpsbT7uqgvsVNQVbi4W8cgs65YD5/gP21rby0ooQdZY389srF\nJMTZhvXdIjJyKfCIyKhU6wyuwGakFTEtdQqvlrxJvDWOLxkXcXL+SZhNZsYnF0S5yrFh3Y4a3B4f\ni2cPT3hsavMAkJrYcxGJaYXB+VY7yxs5eU7esLxfIi8QCHDnM+upbnDy3XNnsnF3LWsdNfzquyeR\nnWYftvfWdJm7U1LZQk2jk3a3l3fW7+ecxROH7b0iMrIp8IjIqLS/tQIILjk9P/s4inILybWMIyU2\nOcqVjS0vf7CXf72/F4AF07OxWSM/9bOpNRh4UhJiepwrzE4kLsbCjrLeF6aQkamsupXqhmD4eOzV\nbVgsJnz+ACuLqzh/6aSIvWfN9mqeeWsnZyws5MwTx3cLPKWVLbS7g3t0vbW2nDNPHI/VoqnLIsci\n/c0XkVGl1lnHo1ueYtnuVzGbzExIKsBitrB0wokKOxFWUdfGshV7w19X1Q/PBqDNoR6e5MSegcdi\nNjMhJ4nK+na8Pv+wvF8ib8OuWgCKxqXg8wfwdATb7sPiyogsQBEIBHjpg708sGwLDS1unl2+ixWb\nKnoEHmco8DS2eli1tWrI7xWR0UmBR0RGjY8r13HbyrtZV72JCcmF/Gje98jRSmvDZtn7ewkEYPr4\nVADKh2kD0KY2N9B7Dw9AenJwqFtji/uInvvaylKWrysfWnEyKBt31WIxm7j8vJnhXsHs1HiqG5zs\nqWge8vM/3FLJsvf3kpEcy5UXziYhzsrjr21na0kDVouJzJQ46lvcuDw+slPjMZtMvP5xmVb7EzlG\nKfCIyIjV6G7i/g2P8tyOZQC8U/4BAQJ8a9ZXuH7+VRSlRm5ojAS1uzrw+wPsq2ph9fZqJuYmce6S\nicDwbQDaOYenr8CTlhRczKC+j8Dj9fnp8Pp7HHv+nd089caOCFYqA9Hu6qCkooUp41LITI3nzBPH\nM7UghS9+pgiAlVuG3tOyfN1+TCa4/isnsHB6Nj/6/HFYLSZanR1kpsSTZLeFew7zMxNYMD2L8ppW\ntpY2DPndIjL6aA6PiIxILq+L3619ILjXTj2cM+kMqttryI7PZH7O8dEub0xqdXbw//7wPguMLLy+\n4E/CL/7E5PDKWsMZeOJjrdisll7PpyUFe3gaegk8eyua+c1T6/D5/Hz6hHF86TNTsVrMVDVo48lo\n2VHeRICDPYMXf2IyAD6/nyS7jVXbqvjiZ4oGPJ9m94EmVhVX0dk30+H1s7eimblTMsgOrdxXVJDC\nFRfM5r4XN1OQlYDL4wvfHx9r4bQFhXy8rZrXP97HrInpEfusXbU6O4i1WYZlnpuIdNfU5gkPWTUB\nGb2s8tmVAo+IjEiv7n0zvLEowKqKNTi9LqalToliVWNb5xydNY4aAKYVpDBrUjomk4kku439wzWk\nrdXTZ+8OQHo/ged/q8vw+vykJcWyfN1+KuraufLC2eyvOVhrh9fXZ5iSyNuxL7g/1rTC1G7HLWYz\nJ83I4c215RTvree4oswBPe/Zt3axa3/PRStOW9B9Fcbjp2Zy27dPJDkhhqf/d7BnLz7WyqS8ZKYV\nprJlTz1vrinjtAWFR/qx+tXmCv6wYNakdK79on4gIzKcymta+fljH9N1hOqJM7K5+TuL+7xnUIHH\nMAwz8ABwHOAGvuNwOHYdco0d+B/wbYfDsT10bB3QOXh3r8PhuGww7xeRse1AayVvl68gMy6db82+\nlDvX/Ik3970HQLY9K8rVjV3N7Z5uX1/0icnhTVsLsxPZWtLAvqoWxuckReydXp+fVmcH4zIT+rwm\nLTSHp77F1e14S7uHNY5q8jLs/OzrC3jkla2s31nL7U+uYUKXGludXtKSFHiOFkdZIxaziSnjUnqc\nWzw7lzfXlvNRceWAAo/P72dfVQv5mQl87/xZ4eOxMZZw705X+aE/RwlxB7+9iY8N/vorp03ld89t\n5O9v7mRaYWpE/xzXhfaSKt5bH7Fnikh3Xp+f4r31lNe0EgjAnMkZpCXFUry3njXba/q9d7D9rhcC\ncQ6HYzFwA3BP15OGYSwA3gOmdDkWB5gcDsenQv8p7IhID4FAgH84/oU/4OeSaRcwPqng/7N33uFt\n3Gee/ww6AZAg2JtIipREUoWqlmTLRW6JZTsbpzltd9Oz2WzNJbd3u3e53cvd5rKXzeZy2WxJuThx\n4kClz+AAACAASURBVNhJHCdxj6tkdas3ir2IvaN3zP0xmCFAgiAIVknzeR49DwVMwwAz83t/7/f9\nvuSb8nAEpbkSNeBZOuRaGoDG2nzqKu3K/x/YXQnAT15pWdTCb5c3BIAtiUObjFzDMz3Dc7JpmHBE\n5K6tZWQZdfzJe7fw0K1VDE/4ePvqsLKc2xdatONVSY0vEKZ70EV1aTZG/cwgs7okm+I8M2dbRxU5\nSioGRr0Ew1Fqy3JYU2RV/iULduKJbzJqjgU8lcXZPLi3CkCxzF4s5N8xoBojqKgsEYfO9/OtX17g\n6YMdAHz8QD0fP1DPQ7dVEZ3juss04LkdeAmgubn5OLBr2vtG4D3A1bjXtgLmurq639XV1b1eV1e3\nN8N9q6io3MCcHDxDu6OTrQWb2FzQgCAI7CndqbxffJMHPFFRxOufe6CYCc5YP5xPP9zA5x/ZnPDe\n5pp8Gmvzaet1MLhI9tTdgy6++J0jAOSkkLRlm/VoNcKMgOdcqzSjt6tecurTCALvu6uWe3aUJyzn\njstcef0hLnaMLcrxq0AgFOGHLzTRNyrVd7X3OYiKInVr7EmXFwSB2zYVEwpHefXUtTm33zkoTXRU\nl87Pct6SNRXwyBkekH5LsPhBsOw0KP0dTLGkiopKpgyMTT17inKzlPrOPQ3FWOOu+WRkWsOTA8QL\naiN1dXW65ubmMEBzc/MRgLq6uvh1vMA/At8H1gMv1tXV1cnrzIbdbkanaq9TUli4eGl5lcxRv4eF\n4wv5+fWR5zFqDXx274cptEjn9F2Wu3mh8xUANlauJcdoTbr+zfAdPPHyVX72u2a+8Rd3sqFy5qBy\naNxLbrYx6ez6XASi0gzZtvoSypMMMPdsKeVC+xjjnhCN9cnP9Xy+g1fP9Cl/lxRaU66bn5uFwxNU\nlvH6QzRfm6S2wkZdbWIQ/Jn3buVs66gSIGkMOmW9//vUWV452cO//NU9rEkiaTrfOkJFkZV8W+oM\nwmpnua6Fk1cGeevCAFqdli9+dCfX3paCmN1bSmc9hg++s4E3z/Xz/LFuHrpzHcV55qTLjTl8vHRS\n2t72huJ5fabSoql7RHHcb6uiVMrsRAVhUc9RhEHlb39k6vzfDPek1Y76Haw8i/UdaOKMTmzZxoTt\nfuev7km5bqYBjxOIP3rNXIEL0AK0NTc3i0BLXV3dGFAKpJzimZhYmkZ3NwqFhdmMjLhW+jBuetTv\nYXE4P3IZZ8DN/ZX7wWtgxCufUx13VdzGsHeUgFNkhJnn+mb5Dl44KjUC/d+Pn+LvP7MHTazGBqB/\n1MN//f4Jbttcwqcf3jjvbQ/FZukjwVDSc1lolWbTLrSMsKkyd+b78/wOWrqleofNa/PYXGVPua7N\nrKetz0H/gAO9TsOpq5KcbXN1XtL1vvZHezl1dYTvPXeF/iEXIyMuolGRYxcHpH13jmGapnHoHnTx\n3x97m131RTMyXNcTy3ktXOuX5j7fvjLI4JCDs83DCAIUWg0pj+G9d9bwg+eb+PnvrvKhe9cnXeYr\nj73NwKgHvU6DRaeZ12eKhKZc2kKBsLJuNCgNVQZH3LNur2fIhcmom1M2F0/f0NS2rnaMUmIz3jT3\npNWM+h2sPIv5HfTHzGgsJh2P7KtOst3ZndoylbQdAR4EiEnTLqaxzieJ1frU1dWVIWWJBjLcv4qK\nyg1I26Sky92YXzfjvUc3PMKfbvv0ch/SqkMehA2Ne2nrTXSuOt0iSbyOXhqcsV46ODwBtBohQQ4U\nz5piK4IA3YMLbxwJ0DviwWzU8YVHt845uKwpy0EUUeRoPcPSg29DxczCeAC9TjtDvtTW51D+diaR\nHb1xVso4dfTPdARTSY7DLWXRPP4wV7om6Ox3UlWcnSAjS8aejcXkmPUcuThAMC44iWfMKRkB/OX7\nG+dt9WxNUsMDc0vaRFHk6z87yw+fb5rX/uJ/T4sl+VRRUUlk3OnHYtLx7b+8k4Z52stnGvA8A/jr\n6uqOAt8EvlBXV/eRurq6z6ZY5wdAbl1d3WHgKeCTaWSFVFRUbiLaJjvQCVqqcypX+lBWFFEU+X/P\nN/F3PzzJ88e6Et6LL45u7pFsuydcAZ56vZUXj3fPue3BcS9HLw0kLax2uIPkWAwJWaN4jHotZQUW\nuofcRKMLK8wOhSMMTXgpL7QoTnCpuHVTCTAVzI05JGlSQYpAySoPbmPn7GzrlItPfM0FgNcf5sQV\nqSHmuDOg1mGkyWTcefrVoQ4iUXGGHXUydFoNtzeW4fGHOds6OuN9URTx+MLUlufMe2ADYMmKd2mb\nknfKAY/Lm/z79QcjePxhRh3+pO/PRvzvZUgNeFRUFhW599aEK6CY2MyXjCRtzc3NUeBz016+mmS5\n/XF/B4GPZLI/FRWVGx9f2M81Vz81tioM2tTFh8vN2dYR3L4QdzSWLel+olERjUbg2rCbwzHpVd+I\nh9s2lyrFmS5vkGyzHpc3RMu1STr6nXz7VxdwuBMHcF5/CLMp8Tw290zwD0+cBaCi0JpgyyuKIk5P\nkNIU9tAA1cXZ9I14GJrwUpqfetlU9I96EUWUpqZzsabISkWhhfNto7h9IcYcfgRhqilpMuRZfrcv\nhCiKCQPr6QHNscuDBEIRcsx6nN4Q3YNOGmvT6xNzMyMbXWg1At2DkrykLoncMRmbqu28cLybgbGZ\nDW39wQhRUUxwW5sPs5kW6HVajAZtwsRBPHIg5PQGEUUxrWAcYNIdwGLSEY6KSftFqaioZIYoivz7\nby9zJqZgyMuZ/Z6fCrUdsIqKyqqgx9mLiEiNrXqlD2UGP32lhR++cJXW3skl20fPkIs/+eYhXjze\nzbnYwHxjtZ1IVOT1M72A1JPE4w9Tlm+hrMBC8zUHX/vpGZyeIHfvKKcoN0uxdx5IMsv88smpksnO\ngURZmj8YIRiOpmwAClN9TuLdcjKhN6bFrihML2gSBIFbN5cQiYq8fXWYUacfe7YRnXb2x5g86PX4\nQwyMeRme8FFbLpkxxEuQRFHkzbN9aDUC771L6qbQNajq/tPB4QmiEQSlp47AzIajs5EbC1Yn3TMD\nBI9fCkji++nMh2R9eGSys/S4ZpG0OWOBUCgcxR9MLrVLup4niM1qJNdqTPp5VFRUMuP1M31KsANT\njajnixrwqKiorAqGfdIgv8RStMJHkojHH2LcKQ1gHn+5mUg0uuj7iEZFfvRSM4FQhGfe6uTXhzvR\nagQ+865NWLP0vHm2j0AogtsnqYCzzXo2rMklHImi12n4yw9s5Q/eUcfXPncrv3dbNQB//+PTPPla\na8J+4gdi0wf047FZ6bkCnpKYo9ZC6xQ6+qWAaz7NH/duLEEADl/oZ8IVID8ntbTBZNCi0wq4vCFF\nznZnYxkCJGTEWnsd9I162LGhkK21+QBKtkIlNQ5PgByLnu3rpYCnvNCadlYm1yoHPDPlZbL1eqYZ\nHq1GQ5ZRi4DUpDSebLOeCVeAN2LXVTzxUrfZZG/NPRN855mLPPlaK8cvD9I34sbjD2OzGLBbDTi9\nIcKRxb9PqKjcbPQMuXjq9dYEy+ncDAOeTF3aVFRUVObNW33HaB5v42ObPoxek3j7GfVJxegFWfkr\ncWiz0jciyW30Og29Ix5ePdXLO3cvbo3RwfP9dA44Kc03K5mT+io7NouBu7eX8+zRLo5eGmR9rEA/\n22zgHbvXEIlEeWBPZYK0rCTO4ve1070c2FulBDEOT5BcqwG3L0zXQOKAXq7/qS1PbgKgbD8/FvAs\nMMPTfG0So15LVUn6AY8928jGajuXu6TapQJb6oBHECQDBo8vxLm2UTSCwPYNhfzyYHuCpO3NmFnB\n3dvLsVmNWLP09C/w890MiKKIwxOkNM/C1nUF5OeYuHVTcdrrmwxajHotk0kkYJ5YBsacYYYH5GBJ\nmFGTlm2WrofHX27G7Q3yrn1rlffipW5OT4iiac7vTV3jfOOp80mbHMZPFjg9QUozPnIVlZubl070\n0DXopGfITTgi8qmHGvjWLy9Ib2ZYPqoGPCoqKgsiKkbpdfdTYS1DI6ROGj/Z/AwAFT3lPFCd6Jkv\nBzyFqyzgkaVX77uzhueOdfPrw53cUl9E3hzZhXRxeII8/WY7WUYt//HD22ntdTDpDrA9JhG6Z0c5\nL57o5ndvX6PELhXoZ5v1FNvNfOLBhhnbi6/BiURFDl/o56Fbq4nGanSqS7KxZ0szZ6FwBL1OS8+Q\ni6OXBqkqzub2LamHaYW5WWgEYUEZHqcnSP+oh01r81JK0pJx6+YSJeDJnyPgASko6ux3MTLpY8Oa\nXKxZemwWA2OxrJ3TG+RU8zCl+Wal9qQkz0xHv5NwJDrv47uZ8AcjBENRbFYD1iw9X//8bfNaXxAE\ncq2GWSRtsQzPHM0EU3FgbxWBJLK0+Nlif4oMjzNJhudMyyhRUeRz795ErtVI16CLrkEn/aMedtUX\nKc6JE6qsTUUlY37+Rpvy9ztuWcPWdQX854/u4McvN3PblpKMtqkGPCoqKgvi1+0v8FrPIT7X+HG2\nFMze+8UfnnI9erHrVYrMBewoalReG/GNYdDoyTGsriZxvbEMT12lHZNRx2MvXuXJ11r540c2p13Q\nnIqfv96GNxDmo/dvINdq5Jb6REmfzWpkT0MxRy4NciTmUCbPUCcj12rkc+/eRF6OiX988iwHz/Vz\nYG8VHl+ISFSM1RkY6BxwcvBcP/ftWqNYPR/YW4lGk/oz6bQaCnNNCwp4Wq5JtVD1aRa3x7NjQyFG\nfQuBUISCNJqD7ttcSnufJJ/bFpNd2SwGekc8fOeZi9izjYQjIvu3lSvfZ0membY+ByOTvgUZM9zI\nOL1BLndIfZTmkkGmItdqZGjCNyO4lGt4rBlK2kDK2CUj/heunxbQJmR4kgQ8w5OSO+DmtfmYTboZ\n9UqjsfcnXarLn4rKfHH7QgnZ99sbS3lfrK5yw5pc/uen92S8bTXgUVFRyZhLo0281nMIgBHvTGtZ\nkMwI9Fo97qCUKSm3ljLqG+MHl35Cf/W9PLj2fgQERn1jFGTlL0oQsZj0jrjRCAJlBWbWFFs5fGGA\nU80jPPbiVT5+oD7j423rdfCjl6/SN+KhqiR71sEZwP23rOHIpUGOKQFP6kHg7gZJVrR3YzGHzg9w\nqWNccbaxWQy8Y3clp64O87NXW8mxGGiOBSB1aRabl+SZOd8+htsXSpgtT5erMTvtujX2OZacicmg\nY8eGQo5dHqQwjQzPno3FPPVGG4FgRKkzscYCxtPNUl2PQadhX9ysYbxsTw14pphwBXj19DUud47T\nM+RWXs9ZSMAT0+M7PcGErKmc4VmIpG02wnGW6r7A9AxPvKQtecBjzdLPelypjBhUVFRS81++d1y5\nBt9zZw3vitWkLgZqrl5FRUUhGAnxk6Zf8G8XfsiZ4Qspl50MOPhx01PK/12hmdaykwEH/3TmX/n+\npZ/Q7ZKcxt5ZdQ9f2vmn5JvyeLHrNb538XEmAw4CkeCqq98JhiL0DLkozTej12nRCAJ//MhmKous\nvHVhgNbezBtUnm0bUeqDPv5AfcrMSmVxNg1VdkW6nJ1mkLE/FkS9ebZPmTWzWQwU5WbxhUe3YTRo\n+d6zV2jumaQkz4zNml4x6ELreJqvTWLQa6guzSyb94G7a/nQPeuoq5o7YMoy6vjIfet56NYqiuzS\ncU/vIbR7Y3GChfdiGTPcaLxwrJsXj/fQP+qhocpOeUw+WWSfO9M2G7kxV8HpEjDFpW0BkrbZeP9d\ntWysln47vmBiO0CXL860wJPo5BaNioxO+ihM0ftpyohBDXhUVOaD0xNMmHBYO4/6znRQMzwqKioK\nF0cvc2zg7djfTTxnLkSIE4BU5lTwBw2PIooiP7z8BJ6Ql/0V+3iz9wju4MyA58Wu1whFQwx5hmmd\n6ACgKqeCgqx8/uqWP+MHF3/ChdHLZBukgVNB1vwbDC4lV7omCIaiNNZOBWL2bCMP7K3ku7+9QteA\nM20L3unIg6n/9Ud7Kbab51ha0jE3dUuZkVSStniqS3KoLsnmfPsoNWWSHXNObIBZVZLNn7+vkX/6\n+XlC4WjavVNgKiAYGPewriK1ycF0XN4gfSMeNlbbM66PybUaecc8jCOm9096cG8VWq3APTsqePlE\nDw/fWpXwfmm+/PnUgCceORj46mf2UpCbRTQq0jngnJfxxHSUAGGaBMzjk13aFn+Ykm8z8dl3beIv\nv30Yf2BawJNC0jbu8hOJihSnCPDkAE4NeFRU5odcLytTXZqzqNtXMzwqKioKF0avAPCxjR+iOqcS\nT8iLO+TBHfIwEZjk5OAZ/un0v/LFQ1+mbbKTbYVbOLD2PgA80zI8w94RjvafBEBE5Mp4M1a9hXyT\nFNRY9RY+VP9eAI4PnAagzLq6fI1kK+Pt6wsTXl8buxF3LsC6WB5M5aQZvGypzVcCjflIiPZvL0cU\n4bmjXQDkWqayOPVVdv743ZuwWQzsrk/fDnwhGRC5fqeucv5ytsWiqiSbz75rE+vKbfzJe7comR8Z\n2ZhhSA14EpD70sgBt0YjUFtuW5Cxw2wZEa/Sh2dpmhBnGSWrat+0gMftDSpBy3Rb6pEJqT4nVYbH\nlsJqW0VFZXbkelmAmrKcjOTSqVAzPCoqKgBEohEuj10lz2TnluLt7C7ZkfD+ZMDB/zj+j3Q6u8k3\n2anOqeRDde/FpDOiETSKpC0YCaLX6Hmu43dExSj19vVcnWglKkZZl7s2oeal2FxIsbmIIe8wRq2B\n7YWbl/UzpyIaFTnfNkqOWa9kR2SKcrMwG3V0TWve6fIG+eELV9m/vYzG2oKU23d4guh1GkzTeoTM\nhkYQ+KPf20T3kGteAc+ehmKeer1NGdjJjUlltm8oZPuGwmSrzkpJrK4lE0nb1Z751QutBDqt1MPF\nO20wfLPjC4QRBDDoF2+uVA4uHJ7pkralq+EB0OukHk2+4MwantICC6FwNKF4GqYMC1JJ+Ix6LTlm\nPQNjMzPeKioqs9M7LGV4/vsndyty2cVEzfCoqKgAcGmsCV/Yz5aCjUkL8XONNj61+fd5eO07+fKe\nL/HJzR/FrM9CI2iw6My4Q26GvCP81Vv/nR9c+gmnh89TmV3Og2vvV7axLrdmxnYbY85ue0p2Egpq\n+OJ3jvDjl5tXvHFfR78TpzfEtvUFM+prBEGgujSboQmfMhMdFUV+8HwT59pGOdk0POf2nZ4gOWbD\nvEwPqkqyuXNr2dwLxmE0aHnnLWuU/y/EUUsmx6wny6hTMjz+YJgfv9zMmea5P3dzzyQGnUbJkq1W\nDHptUkvjmxlfIEKWQbeoxiJytiheSgZSHx6jQbuktuAmgy4hw+MLhAmGo+SYDWSbDbh9icc0lEaG\nB6ReVuPOAMMTaoZQRSVdekfc6LQCpfnmOd1CM0ENeFRuKCLRCL9ue4HhWRzDVJIjiiIvdr2GgMAd\n5XtnXW5jfh0H1t6LXpuYarYaLLiDHg71HiUUDXF25CIAv1d7gDLrlPvVBnvtjG3uX7OPPSU7eUfV\n3bT1OZhwBXjzbB//+4mzOFZQBy/L2batT579qCqW6hauxWalXj7Zw4V2yd5Z7hI/G6Io4vIGF+Ru\nNR8O7J2qd1mMfQqCQEmemeEJH5FolFNXR3jzbB9/+91j/OLNtlkDBbcvRO+Im9pyG3rd6n78mAxa\nAiE14InHHwwrUrDFQnYcjA94nnilhZ5hN9Ylyu7IZBm1ikwPYFgJaExYzXrcvlBCg9GeIUnCWlGY\nevZ5fYWUvWzqHF/sQ1ZRuSGZdAfoHfFQlm9ZskmO1f3EUVGZJy0T7bzS8ybPdby80odyXXFprIlr\nrj52FDVSakm/U7qMVW/BG/ZxtP8kRq00oN5gX0e9fT1ZOhPF5kKy9dak28412vjDjR/EbspVUtoV\nhVba+hx85Uen6F5AncxCONs6ikGvYeMsTmB22U7XG6Ktz8GvDnZgsxgQBHD7Q0nXkfEFwoQj4qJk\nW9JBr9PyzT/dx9994pZFe5iU5JmJREVGHX6ar00or794vIf/+v3jnGkZQRRFwpEoobA0qJyq31m9\ncjYZg14NeKbjC4QxGRc3CLFk6RGEqXqZgTEPr56WHB03rV1aE5Msgy5BtjgiS9Zys7Ca9IjiVI1P\nVBTpHHBRbM9KcPRLxvqYkceVzrElOnIVlRuLJ19rJRyJcue2+SkY5oMa8KjcUIwHpIHXxbEmgpHU\ng04VCVEUeb7zFQQEHqi+d97rdw446e6VMjHBaIi7K27nizs/z6c2fVSRvnx2y8f48+2fRSOkvuXI\nRYt/8f5GPrC/lklXgH/7zaWEWdbFpLlngr/616McuzyY8PrQuJfBcS+bqvMw6JPPaFtjM9PDE17+\nPXaMn/29TVhMejxxUphJd4Bxpz9hXbk2YK5+OouJzWqksnjxbD7jrambeyaxmHT8/KsP8dCtVUy6\ng/zzry7yzV+c5wvfPszXfnoWkORssLrrd2SMei3BUHTJfnvXG6Io4g9KkrbFRCMIWLP0SobnXKuU\nnf/UQw18/EDDou5rOllGHYFgRLEpH4pJ0IrsZuX6dseOa3jChy8QTkuKWVWSjUGn4Yqa4VFRmZNL\nHWOcbBqmpiyH/dtm70e3UNSAR+WGYsIvDaiCkSBN480rfDTXB/HZnXj5Wbo8e6QLr2cqKGjIr6PG\nVo3VMCX7KLEUpbXt3hE3WUYdeTlGDuyt4rbNJQxN+Gjqmphz3Uw40zLKqMPP9569olg+A5yPSdO2\nrZvdeCA7S8rOvHVhgDFngAf2VNJQZcdi0iVI2v7pqfP8x389ymMvNjHhkgJDuaHhcknaloLSmFPb\n5a5xRh1+NqzJJcuo43131fKVT+2mocrOpY5xPP4wnTFzB3lAuabIumLHnS7GWKAbVLM8AITCUSJR\nEdMiS9pAquORMzxnWkcQBNia4tpbLLJi2SpZ1iZneArtWUqvK1ds8kL+DadjlavTaqgpy6F70KnU\n+KmorBSiKCImmbhxeIJ85bG3OXV17trLpSIYivD475rRCAJ/+M66JandkVEDHpUbign/VCPIuRpn\nqkg3whcWkN0Zdfg43z6KGJ7KVFTlrEmxxuyEwhEGx71UFFqUzNDdOyoAeONsX0bbjGdk0kdzz0TC\njb9vdMr3P146d75NmmWO778zHdkyU9b9162RpG9mkx6PP4QoikRFkYExD6IIh84P8J///Ri/PdKJ\nMzZrfD0HPLI19bFLUnYsPmtTmm/hSx/axp+/rxFt7AEWjkRxeUPotIIy0FzNGGPueYHQyppnrBZk\nN7PFzvCA1EjX4w8z7vTT0eekbk3uolvSJkMO3vyx5qNKDY/NpOxfzvB09EkBz9o0m+Wuq8hFFKGt\nzzn3wioqS8jzx7r582+9lWCzPuEK8MaZXroGXTzzVkfSgGg5ePZoFyOTfu6/pWJRFQjJUAMelRuK\niYCU4ck12rg02kRIlbWl5NJYEz2uPrYXbUk7uzMw5uF4TAJ28Fw/ogiIU7Myek1mA6LOAReiKNXv\nyKwtzcZmMSjFwgvhO89c5B+eOMtXf3JaqSWJ9/2Xmyr6AmFark1SXZKt9NRIxvQBmVyPY8nSEY6I\nBMNRXJ4gkajI9vUFfOJAPdYsPb9+q5NzMUOE5arhWQqK7FkITNkH15QlNiAVBIFt6wvYvl6aqff4\nw7i8QbLn6Uy3Uhhj1stqHY+EXMuy2KYFMCXtPHxhAJGZfa+WCjnwlj/b8KQPe7YRg147FfD4QoTC\nEU40DWEx6ahOs8nqhlgdT2vv5BIcucpy4vGH6Oi/PgNXURQ5dL4fjz9Ma680Ifza6V6++J0j/PZI\nFwADY16udi+NiiIVk+4AL53oIS/HyLtvX7vk+1MDHpUbionAJFa9hV3F2/BHAjSNt6z0Ia1a4rM7\nB6rvS3u9H7/UzHefvULPkItD5/uxmHSU5kqz+3W2DRkdSzQq8tTrbQDsrJsa7AiCgNmkS3BSyoTh\nSR89Q27MRh3tfU6+9tMzfPXx0zg9QUpjtShyDcHlznEiUXFOSY11Wv2N3E9EbpTo8YWYiLnM5eeY\nuGNrGZ96SKpJOHZ5CEi/6ehqxKDXkm8zAVIdRmVxcpmaJTZw9PpDuLyhZa1bWgiKpE21pgamggLT\nUmR4zLI8tB9ACZKXGjlb5QtE8AXCTDgDFMUsp5UaHl+It68O4/aFuGNrGXpdegFfbbkNjQCt19SA\n53rG6Q3y9z8+zd//+NR12Yj42rCbUYdUQ9o54KRzwMmTr7Uq768rlwLzkysgaxsa9xKJity6qWRJ\n7ivTWf26AhWVNBFFkQn/JCXmInYUNfJqz0HODF+ksXDTSh/aqmQqu5N+7Y7LG6QlNmP5xCstuLwh\nHthTSZaxgt82+di2+24ee7GJOxrLqC23zbG1uGPpHKdzwMnuhiI2Vic6M5kMWkYm/bOsmR7nWqSM\nyqP3rKO8wMJTb7TRFpvt2liVx8CYV5GunG+fW84G0oDYoNMQDEcRhKlBmyVmpevxh5WaHdnRraHK\njj3byIQrgM1qWPIU/lJTkmdm1OGnotAyq7mDHACOuwIEQhHlPK125IDHr2Z4APArGZ6lCHik38iY\nM0BlkZWCOfrcLBZytsrpDfLMrzoQmXIQlGv0XL4gp5qHEYC7t6dfUJ1l1FFdaqNjwEUoHF31Nuwq\nEm+c7aOtd5KN1XlsX1/IPz11Tuk39vzxbjSCgEGv4T131FwX0twTV4aUv692T3DiyhDRqMhffqAR\nUYSashz+4v8eZty5/C0g5EnG5Zr4W/3flopKmnhCXkLRMHaTncrsCvJNdi6OXiEUDWcss7qRef3a\nYQAOpFm7E42KvHG2D1nq29LrQAD2by/H6QnyzKE1PPa8lKVp7XXw95+ZvZ/PdPpHJWnZLfUzbatN\nBh3hSJRwJJqxpfLZ1lEEpEJom8XAFz+4jT/+xkEAastzePOcgNMbJCqKXGwfw2YxUJWGdMVq1jPu\nDJBjNijFlrJlrdcfYtItyeRyYwGPIAh8+qEGjl0Z4j131CxZF/nloiTPzKXO8ZSF3HIAKM+ODaQN\nXQAAIABJREFU5lwvGR6lhkcNeGCqhsdkWBrTApntG5ZHzgZT2aqf/K6ZSXeQ7esLeNe+akCSpoKU\n8e0ZctNYmz9nw9HpbFybR0e/g+5BF+sq0p8AUlkZoqLIE6+0EImKnLgyzCtF1+gZcrN3YzHHrwxx\n+MKAsmxVcTb7tpSu4NGmxusP8fTBDt4424c1S49ep6E9Jst7+LZqGmunsqhGvXZFet7JjX2nqyWW\nCnXKQeWGQa7fsZtsUv1A0Rb8ET9XVVnbDAY9Q7RMtLE+t4Zya+qbtiiKdA+6+PIPTvDrtzqBqdnv\nLbX5FOVmUVuWk9CMb76ByXDMHanYPnNAIQ+wMpW1BYIR2vocVJVkKzUzRr2Wv/n9nezcUEhjbQHZ\nZj0ub5CuARdOb4jG2nw0adSZWGPBjc1qiHtNGii5fXEZnrhaoIbqPD75YIOS9bmeqYi5ra1Lkc2T\nJW2DY1LAc71leFRJm4RvGTI8ADuWMeAxxz7LpDtIQ5Wdz717E1qNJnZM0u+0Z0gyNrknZqAyHzau\nlbLEah3P9YHHFyISsyiPiiI9Q252NxTx6Yc3TtVoxu7vA2OrU94WFUXeutDPX3/3OG+c7aOswMJf\n//4OpTdU3Zpc3n17dcI6NqtBaZWwnMgOiNnLYFACaoZH5QZiPGZJbTdKkoTthY281nOIs8MX2VKw\ncSUPbdXxSo+U3biz4rZZl7nQPsrbTcNc6Z5QBu5ba/PZsCaXa8Nujl8Z4p4dksRDEATu3lHB4y9L\nVuDhyPycrYZjdsXJZlDlWVh/IJyRc1Nbv4NIVKR+WgPRdRU21lVsAaTBzcikL86dLb0aAnlmKjcu\noEnI8EyTtN1o3La5hCyjjh0bZj9fygBhXA54rpMMj17N8MTjX8oMT+y6LrCZEiZOlppsi7TfmrIc\n/ux9WxLqc8xGHYIAoig1It1cM/8mqBtj67T2OjiwOIesskACoQhnW0bYvbF4xqSWLLHav60Mm9WI\n0xvkw/euR6MR+MSD9bx6updH717Hf/vBSUXmtproGXLx+MvNtPc7Meq1vH9/Le+4ZQ06rYa7tpUT\nCEb4wwfqlaBexmYx0DbpIBoVl9QWejqyjHy5MjxqwKNywzCV4ZECnuqcNdiNuVwYvazK2uJom+zk\n+MApyiwlbC1IXt/k8AT51i8uICK5ke1uKGLvphKlL82Yw8+Wmny21EzVudzRWEogGOHFE92MOwOI\nopi2G9fwhA+b1aDIiOKZso7NbODZ3CO5z9RXzt7sMtus59qwm9MtI+i0Ahur7bMuG48cgMW7rclS\nGI8/rJgW5KZwe7ue0Wk13FJflHIZOQC8XjM8ag2PxFJmeIrzzGg1ArdtLllWB7+GKjuff2Qzm9bm\nzSia1mgELCY9bl+I/dvL08r4TifflkWBzURr7yRRUcxoGyqZc+h8PyPOAO/ZV60M5A9fGOCnr7Rg\nMupm9FmTbZutZsMM17DG2gIaawsQRZEso46BMQ+riVA4wtd/dhaPP8wt9UV88J515OWYlPcbquw0\nVCV/rtmsRkRR+vypnEkXG9kZdTks6EENeFRuICZjPXjkDI8gCGwv2sLr196iebyVzQVL27V7ubg6\n3sqAZ4j9FfsyGhy80v0GAB+ufx9aTfLZ2s5+JyLwwJ5K3r+/dsaDOt9m4lZbotGBTqvhgT2VNPdM\ncL59DG8grBSspyIciTLm9LN+FlmU7KSUacBztXsSjSCwviJVwCMNwvtHPWyqtqc9qJMLm+MlbYpL\nWyzDk2XUJQ3kbhbkDM+YUzKeuF4yPAZV0paAL9arZin68OTlmPjHz9+27MGwVqNhV4qA3Z5tJBiK\ncHtj5rUa6ytyOXZ5kIExL+UFy5e9UoGXT/YwMOZFi8gjd9QAKGqFCedMI5ypIvrZ71GCIFCab6Z7\n0LWgutLFpmfYjccf5s6tZXz8QP281pUn7CbdyxvwyBme5bru1YBH5YYhvoZHZntRI69fe4snm58h\nv+fNJdu3Xq8jFAov2fZlomKUDkc3ACatkVvLbpmxTCga5omrv8QRcHL3mtsT5HyRaIS2yU6KzYXU\n2Kpm3U9HrKt4Q5V93rOS8qzSuDOQVsAz6vAjilJ382RM1fAknt9zraP8/I02/tNHts96k5Z02C4q\nCi0pg5h4DXHjPDq8y9kcm2Vq/4pLmy/EuCtww8rZ0mX67N31YsVtUk0LEvAHYpK2JejDAyzrQCtd\nPvVQA6FwdEEz0OvX2Dh2eZDWa5NqwLOMRKMiI7Ha0GePdLF+TS6bqvOULI4c3MTjjL031wC8NM9M\nR7+TUYdfacC80nQNSL3q1mdgjiG3VMikjqetz0F5Qern62y4fCEMOo2STV9q1IBH5YZh3D+JgIDN\nMOUYVZ2zhrU5lXQ6e5SA6Hqn2FzIRMDBM23Ps7VwM2Z9YqBwZug8JwfPANDp7OHLe75InklKZV9z\n9+GPBNiVW5NyH10Dclfx2d23ZiMvRxq4jDv92KwGHn+pmZJ8M++7qzbp8nL9TpE9+YNDHnj6ps20\nv3iim8FxLy29jlllVWMOP8FwlLI5BhrxWYetc9hRxyNvN77uQB4cNfVM4guE2bou/e3diEx3orte\nMjxTNTzzq0e7UVnKDM9qZTFs4+XMcmvvJPvnYWutsjDGXX7CEZG1ZTn0DLr43m8v83ef3K04g8nB\nTTzO2IB/LifJkljvtrebhnjo1uplrXuZDbkxak3Z/J/ZObEMz3yd2pp7JviHJ87SUGXnP354+7z3\n6/aGlq1+B9SAR+UGYsI/ic2YkyDT0ggavrTrT5d834WF2YyMuJZ8PzIvdb3Osx0vcWzgbbYWbibf\nZFfkbQf7jiIg8ODa+3i+8xX++dwPeM+6B9mc30DLRDsA6+3Jgw+QXNk6B5wU5poymtnMy5YyPOfa\nRnn8d82MOwMU2ExJAx5RFHn1dC/ArB3M400LZMYcfqVrtBwwJUN20pEfULORHbvhl+abZw28knFL\nfRGVxdkJs3w5FgPF9izFhrm+Mr16oBsVo16LViMo7kfXSw2PQS9JVQKqpA0Ar1+6/q53K/XlpjTf\njMWkU+5XKsvD8ISU3dm9qYS9DcX87LVW/v03lwlHpQmMZBkexTXMkvoetbW2gN8e6eKZtzo52TTM\ne++qYdu6gmWtP5tO54CTLKOW4gwyTnKN6XwzPF2D0pinqXti3vsEyZa6OG95em6BakutcoMQFaM4\ngk6lfudG5/ayPQgI/KrtOf722Nf4v+e+R5ezh6vjrXQ7r7G5oJ4D1fdxZ/ltDHtH+LcLj/FPZ/6V\nQ73HAFifO3vA0z/mxeMPZ5TdgakMz8Fz/Uw4Axj0GmVWbTrnWke51DHOprV5bF6b3AUpK4lpwdtx\nXaHlB1sy5MLSsvzUGZ7cmCRta5rubDKCIMyQNAiCkNBLJJVZws2AIEwFO+vKbddFsz5Q+/BMx+UN\nodUIS+LSdiOjEQTqKu2MOvxc6hhb6cO5aZCfC2UFFu7bVUFjbT7N1yZp75MyIa4kGR6XJz1JW0WR\nla9+Zi+3N5bSP+bh209f5Bdvti/yJ0ifty70MzjupbokJyNjDJuS4ZlfwLOQyaBgKLLsjajVgEfl\nhsAZdBEVown1OzcyVoOFPaU7Acg12miZaOPrp/6Z7178kZTdqb4fQRD4YN0j/M3uL7C1YBMdji4m\nApM8vPad2IyzSzXOtowA0DgPaVc8srW0xaTjLx/dSm2ZDX8wktSq+silQQA+ePe6WWfHlAxPXA3P\niStDaDUCAnMFPOlleDbX5PHBe9bx0G2z1zXNB7mXSF6Ocd7NCm9k7tl5/Uh6TCtoS90z5FqRRoDx\niKLIK29f442zfYBUk2bN0q/oLPb1yu/tq0YjCPz45eZ5W/arZIb8XCjNtyIIAhurEyfUktfwhBAA\na9bckzL5NhOffLCB//GpPRTZs3j5ZA+dMSn4cjIy6eOxF65iMel45I61c6+QBLkxttTCIf3fp+xg\nqdfNP5RwL3MPHlAlbSo3CEoPHtPNM5v+6IZH2FW0jbq8dbRPdvHbjhfpcHRzb+WdVOZMNckrs5bw\n2caP0ePsJRgNsS439U3xTMsIGkFg6zyK9+PJyzHxHx7dSlmBhbwcE2/FulN7fKGEwuRAKMKljjFK\n881KA8tkTK/hGRz30j3korE2n74Rt9K0NJ5AMMIPX2ziZJOUCSqeQ6am02p45+7K+X3QFNSU5bCr\nvoh15TZ1gAjs3VTMxfYxdtWltrBeTRhWKOB5/lgXTx/sYNu6Av78/Y3Luu94nj3Sxa8Pd6LTarh9\nSwluX+imN+DIlMribG7dVMyRS5Jb25oU9zuVheP1hznTKk3clRZYCPmDFNpMCcskzfB4g1iy9DP6\n1KSirMDCxx6o5+s/O8tzR7v4s/ct7zXbNehCBB66tTqlE2kqcswGdtUVcqp5hF+/1Tlrve10FtKb\nSw44l8uSGjIMeOrq6jTAvwBbgQDw6ebm5rZpy5iBV4BPNTc3X01nHRWVVPzw8hO0TrSzu2Qnt5fv\npSBrasZmzDcOcNNI2gCMWgMN+RsAWG+v4T/s+DxD3hGKzMkDlfggKBmiKHKpc5yuQRcNVfa0HNZm\nY3Ncfx75huaeFvBc6hgnGI7O2VndZEy0pT5xZQiAPQ3FHA5HaeqeSBiURkWR7z57mbOtUhNRg06T\n0QzUQtAIAp9/ZPOy7nM185mHNxKJiqvGwjUdFNOCZazhGXX4ePpgByAVua8UL5/s4deHOwHJNr61\n14E3EFYH6gtAzjKPO/3qeVxCRFHkf/74FMMTPrKMOmxWA6P+IPnTAx5faEZvJJc3lJGpSkOVnQKb\nieaeyWVv3nlt2A3AmuKF/aY+fqCB7iEXzx/rZn1FbloKD1l1kYnLWn9Mbr6ckyiZPn0eAUzNzc23\nAv8Z+Eb8m3V1dbuAQ0BtuuuoqMzFlbFmHEEXr/S8ydfe/j+4gm7lvbeHzgJQm1u9Qke38giCQIml\nCI2Q3mUdjkQ52zLCrw518M2fn+cL/3yEb/78PDqtwAN7Fi/bIcsDptfxnGmRsi9zBTxZcbbUoihy\nsmkIvU7DtvUFFMWsrEfisjxPv9nO2dZR6itzuXNrGR+6d/2ifRaVzBAE4boKdkBqPKnXaZY1wyM7\nLQErlhl881wfT73eRq7VoFw7p2My1+Wcjb3RmLLrn9n/RWXxGJ7wMRgzjPnkg/XKdVRgS5QWiyJ8\n+h/e4FSsHjQSjeL2hTKuKamvtOMNhJUAZLnolQOewoUFPGaTjs8/sgWdVsP3nr2c1u9UsarPIMMj\nS+e3ZCidz4RMn0C3Ay8BNDc3Hwd2TXvfCLwHuDqPdVRUZsUfDuAN+1ifW8P9lfvxhf0c638bgGHv\nKFfGmlmbU0VlduoshsoULxzr5tu/ushzR7u42DGGXiuwc0Mhf/37O9lSs3g3IatJzvBM1eCEI1HO\nt42Rl2Oc1Z1NRunDE4hwbdjNwJiXrbX5ZBl1ilStd0S66R8638+LJ3oozjPzJ+/dwscP1KtWsCoZ\nY9RrVyzg8fhC89LTLwbDkz5+8nIL1iw9X/rQdvZtKUEATscGhRY14MmYfDngca1sbdaNTlOP5Bj2\nB+/YwM44Ce1s7oJPH2wnGhUZnZQG+AXTMkHpUl8lqUuu9mTmWJYp14bd5FgMirX0QqgqyebD963H\n4w/z/eeuzLm81y9NYhpSZHiCoQhjDt+M1y52jFNkz1rW3lSZ1vDkAPEei5G6ujpdc3NzGKC5ufkI\nQF1dXdrrzIbdbkanU11hUlFYuPBeAaudXmdsFiOvlI9sfReH+o9xZPAEH975MEdGjyIi8mD9/hU9\nF9fb93BtVEopf/mTe6irsi9Z47/SWC8LQafFbDXh8YcYGPfhDYS5d3clRUWp3eBkh6+ICBe7pIfJ\n/XurKSzMZt/2Cn7+RhtXexxUtY3w+MvNZJv1fOWPbqWsQJWNrATX23WQiiyTjkhUXLbP1DvqQSPA\njvpiTjUNYcgyKpmB+ZLJMT97vIeoKPKZR7awtaEEgOqyHDpjgVhxgeWG+n6XA/l8RbXSOMYTjKjn\ncAnpGpTGCrduq1DO8/TzbTJoFYn00ISPlgEXhpjseV2lPaPvZ992Hd9/romuIfeyfb9uX4gxp5/t\nGwoXbZ8fuL+OCx1jnG8dJSBCRdHs2/XGzqGgEWbd/7eePMvBs7184y/uZG2ZZCp1+uoQgVCE27eW\nz/n8X0wyDXicQPyn08wVuGS4DhMpemyoLH//l5WifUxyCjKLFryOCLcUb+dw33Fev3qS5iFJa16k\nLV2xc3E9fg8dfQ7s2UbWFlkI+oKM+ObfZTkdorEZ8sERF//t347Q0utgd4M087ZxjS2t82bQa3B6\nApy6MoROq6G60MzIiAurXqDAZuLQuT5OXpEc3z7/yGb0onjdfR83AtfjdZAKrSDg9YeW5TNFolHa\nrk1SVmDFFqsj6OwZJ5JB88tMvodQOMLvjneRY9ZTX56jrF9iz1ICHiGqXlfzIf57iEaiCED/sFs9\nh0vI+bYRbBYDRkH6rSa7FuJbHAgCPPnyVW6JPZNyTLqMvx+jQUvfMn6/cka40GZa1H3uqS/ifOso\nz7/VznvvnN3AYDKWrfT7w0n3HwpHOXy+j1A4yjefOMN/+YOdaDQCl2VDCXvWop+rVIFfppK2I8CD\nAHV1dXuBi0u0jooKIDUVhSkXtrvKbwPgUO9R+twDmLQm8k03d4PH+eD2hZhwBZaleDbetKAl1nzv\nZNMw1ix92q4yJoMOlzdI74ibqmIr+ljWVxAEpQbIH4zw8QP11N3kjT5VFg+DTkMgvDyysqFxH8Fw\nlOqSbEWe4pxnI8CFMDzhw+MPs219QYLJR2lcDyu1hidzdFoNNqtBreFZQkLhKA53kLICS9IaOPm6\n2re5BK1G4AuPbuWW+iJ6ht0cPNcPSI1iM8VuNTK5jHbynpikLBOjhVRs31CI0aDl2KUhoqKYdJlo\nVFT2H5rlHtnUPY4/GMGg19I54OS1M1KTcdmwoKwg83OdCZlmeJ4B7q+rqzsKCMAn6urqPgJYm5ub\nv5vuOhnuW+Umo889QLujC4C8WMBTZi1hfW4NVydaERCosVWp9r/z4NqQNKuyHAGPrPufPnjbvr4g\nbTcbs1GnFKKuLUtMgd+3s4KRSR/vuXs9FcvYtVnlxseg1xIKLU/AMxYbCBfmmqYaAS5jwOPxS4KL\n6bUA8QNANeBZGHk5JroHXTPcwVQWB6W3yywBwF9/dAevne7lA3ev45MPNSAIAjaLgZNNw4w6/Gg1\ngmKEkwn2bCOD415C4YgyKbeUyAGHeQGOqskw6rXs2lDIkUuDPP1mO8FwlA/ft175zQ6MefjN4U7k\nWCg0S2+pMzFjgi99dCffevIMvzrYwY71hQyMedFqhGXvUZdRwNPc3BwFPjft5atJlts/xzoqKinx\nhwP84+nvEIxID367cWr2/q6KfbROdiAiUm4tXalDvC6RnWQqFujskg7yIKl7KDF1vbMutTtbPJtr\n8pSAp6Y0MeApyM3iz97XeMPJqVRWHr1OQ1QUCUeiS+4yJwc8eTkmxRJ+OTM8nthgcbodfUKGZ5Fn\nkm828rKNdPQ7cXqC5C5RzeTNjBzwzGauUZxn5iP3b0h4rbI4m8bafC60j1GcZ55XD57pyN/phDtI\n0TIM5r2xSQqzcfFbau7dXMKRS4O8eKIHgHt3VlCSZ6Z70MU/PHEmQRY4W4and8SDViOwe1MJH7xn\nPf/vhSZ+8rtmBsY8FNmzlt258/ryCVW56WieaFWCHYBck035u7FgI7lG6f9qwDM/5JRyReHSO6SY\njToEoG9ETmNb2FVfNKPzdSru2THlvldTtnxFjio3N3Ihc3AZsjyy1Ck/x6RkWZYzw+OeJeCJn/FW\nMzwLQ+5ov5yypxuR2ZwTlQzPPH+nD91aBSz8eSj3lJlcJic+OeCxzOJAtxAaKu3kWqeyvcOxevqn\nD7bjD0bQxqkzZgt4HO4ANqsBrUZg35YS6itzOd8+hi8QoSx/+dzZZNSAR2VVc2m0KeH/es3Uha3V\naLmv8i40gob1uTXLfWjXNeNO6YY8vRnbUqDRCAmWoO+6rZrPP7J5XrM7JXlmbt1UTFVJ9rKnwVVu\nXmS71VB46a2p5Wsyz2YixxLL8CTpBr9YePwhnj7Yzt989zidA05F0jY9qIm/TtWAZ2GYDNJ9cDmb\n2d5ovHa6lz/+xkG6B2dm8+fK8MzG+opcvvDoVh69e92Cjk0OeCaWOeDJWoKAR6MR+IN31imS1qEJ\nH50DTi51jlNfmcs3/mQft20uIcuoTRrwiKLIpDuIzSKdE0EQ+NgD9cr9pHSZ63cg8xoeFZUlJypG\nuTjWRLbeSmPhRrL1M+VX+yv2cVvZbozahXvQ30xMuANkGXXKA3ipsWeb8PglGV1xhnU2n354o1qn\npbKsKBmeWWYwe4ZcfO/ZK3zyoQbWli4s8zju9CMgFT6DJI7v6Hfym8OSC2VlkZXtczTpTYdQOMrL\nJ3t46UQP3oA0YDrXOqrYv1uyZt4T7ttZQUvv5Ky9TFTSw6iXfk+BZaoLu9GIiiI/faUFgEudY1RN\n6+GWaYYHWJTec4qkLS7gGRjz0D/qSegJtFh4A8mzsovF9vWF2LONfOWxUwxP+LjaLbWFePi2anIs\nBj798Ea+/rOzNHVPEIlGE+SAbl+ISFRMyBIV55l55I61/PLNdmpKbTP2t9SoGR6VVcs1Vx+uoJtN\nBfV8pP79vKv2gRnLCIKgBjsZMOEMkJe9fBryd+2rVv7OVNusBjsqy40+luGZLeB57mgXfaMeXj11\nbd7bHhz38q1fnKepaxyQanhyrAb0Og16nZb8HCPDEz5+c7iT3xzu5F9+fYnwLMXB8+G3Rzr51aEO\nBAEO7KkEpIajcgF0ssHTR+7fwN99YrdaaL9AjPLvaRmb2d5InG0ZVf5OJmtzxzKiK5WJtE+TLIqi\nyL/8+hL/8swlXCmytcevDDLmmL97n2cJa3hkinKlTMzFjjHOto5SU5ZDQ9VULbXs6BgOJ7q5OdzS\n551eq3ZgTyV//5k9bF23eM3N00WdrlFZtVyMydm25Des8JHcWASCEbyB8Ay3s6VkV10htzeWMukO\nLLqjjIrKUjFVwzNzcDXm8HMmNgA70zpKIBRRBrSz8cLxbn739jW++pm9vHGmj/PtY1zoGON9d9Uy\n7gwkzFj/zR/sUow6njvaRVP3BC5vSBlUZYIoipy4MkSWUcs/fO5WTEYdr5zqZWjcq3SYV2VrS4f8\n+5itBkUlOaIo8vqZPp58rVV5bcwxUzbm9sVkmStkrjFd0na1e0KpXR1z+sk2z5ycHRjz8N3fXuGO\nxlI+8eD8xjqKacESZl7NJh3ZZj3DEz5Ayu7ETz7KAU8oEsXI1P1v0iOdg/gMD0gTl6UrUL8DaoZH\nZRVzafQKOkFLfd76lT6UG4qJ2OzTQgZO80UQBD75YAP/4dFty7ZPFZWFYohJkJJp1M+3jxIVRQps\nJgLBCBfax+bc3tnWEZyeIAPjHpq6J9DrNNgsBn75ZjuRqEhezlRNnT3bSEOVnYYqO+UF0gBhoa5t\nPUNuRh1+ttYWYDbp0QgChbkmhiZ8ihxIla0tHUaDGvDMl0AwwveevcJPX2nBbNLxhUe3IgATrpkZ\nEXesebZ1hSbVcix6BAHGY8f26ule5T25Rm868utDsYBiPnj9YYx67ZK7ncnGJWuKrGytTczM6LXJ\n75GTLum7sK0iN0I14FFZlUwGHFxz97PeXotJt/SF9TcT8uzTckraVFSuR+ReGsEkpgWyjfO9OyUH\nweOXB1NuKxoVuTYk1bFdG3LTO+JmfYWNv/3EburWSP3FZpN7Zsdc21y+hQU8p1uGAZRmvQDFdjO+\nQJihCR9ZxqUfPN3MGNQMz7z50UtXOX5liNqyHP7247ewpSafHKtBsXGPZ6UzPFqNhtJ8C70jHobG\nvZxrHVXczJIdL0zJ30Yd8w94PP7QskxQlORJsrbp2R0AnZzhmXaPdCgZntUzzlCnclRWJbI722ZV\nzrboyDNjuWrAo6KSklS21HIfinXlNsoLLVzsGMPrD80q2RwY9yq1QMdiwVF9pR2bxcCXPryNMy2j\n1FfmJl1XbqTo8oQW9HlON49g0GkSCrTl2dsJV0CRtaksDYqkTXVpSwt/MMzplhFK8sz8p4/uUILx\n/FkauLp9IXRaYU5p6VKytjSb/lEPP3mlBRHYv72c1073Krbz05EDnglnYN79vnyB8LI8x991WzW1\n5bakvfMUSdssGZ7pkraVRJ3KUVmVXBqLBTwFasCz2KgZHhWV9DDoZ8/w+GKDVpNRx56GYsIRkdPN\nI7NuqyfORre11wHAhlhmR6vRcEt9UVKNP0BO7PWF2FT3j3oYGPOyuSZfkVYBFMf12VkqtycViSnT\nAtWlLR0udowTCkfZVV+UEAjkZRuJREVc0ySebl8Qa5Z+RQ1uasok97HLnePYLAbeuXsNAGOzSNom\nY8X9IswaFCUjKop4/eElNSyQKbKb2b+tPKlpiSJpm2aoMqlmeFRU5iYYCXF1vI1SSzEFWek3p1xq\nfvhCEyeahpT/5+eY+PLHdi2btfNiIQc8q+lGpKKyGpEzPKGkGZ5YDwyDlt0bi/nVoQ5ONA1xx9ay\npNvqHprZN6SsIL3i3cUIeE63SMHY9FnakrgCYmsSS2qVxcOg2FKrGZ50OBl73u6a9puVa93+7rG3\naazJp7I4m6qSbNy+EPk5K5ulrImzp9+/vZy8HBNajTBnhgdgxOGnyD7Vn6bl2iRjDj+3bi6ZsZ4/\nEEZk5ScpZsvwONxBNIKwYvLCZKh3N5VVx6BniFA0xAZ77UofisK408/hCwNYsvQU2Ew4vUEGxry0\n9TnYvHb57RUzRRRF2vucCAKqfEVFZQ70Kfrw+AOxDI9Bh9mko7Ysh6buiVh38ZmTCd2DLgTkDjuQ\nYzGk7YiWbVm4pK3l2iQws9/IhjU2tBqBSFRUrd+XGNWWOn2ausY53TxCZZGVNUWJPfg6zDiKAAAg\nAElEQVTkgMfhDvLWhQFgQHlvpV0GK4osGHQaIlGR/dvK0AgC9mzjrDU8sn0zwOjkVB2PKIr84Pkr\njDr87KwrVLLNMkrT0WXI8KRiypY68R7p9oWwZulWlZW9GvCorDrcIcnGMceQPceSy8exy4OIwHvv\nqmH/tnLOto7w7acv0tHvvK4CnvY+J91DLnZuKFTtoVVU5iCVpE3O8Jhi8rDdG4tp73dy8uow9+9a\nk7BsVBTpGXZRkm/G4wvh9IYoy0+/0/hCMjzfe/YyI5N+Jt2BpEGWVqNh35ZSDp3vx7FAFziV1Ki2\n1Onz8zfbEQT42IH6GYH41nX5XO4c575dFdizjXQPuugectE34uGOxtIVOmIJrUbDR9+xAY0gKBMf\n+TkmWq5NJq3Ric/wjMb14ukZcjMyKf3f4QlSmJtFz5CLpw92UFFoUWp3LCvsqjibpC1VPeNKoQY8\nKqsOb0jqPWHWzRwQTLgCUvAhijy4t2pZZiRFUeTIxUF0Wg2766VuybJOt6PfueT7X0zeONsHwH27\nKlb4SFRUVj+pJG2+gNR3RxNzYdq8VpLfXht2z1h2ZNKHLxBha202/WMeKeBJU84GUlCl02pSNi9M\nRigc4e2rI0rD0vUVybubP3r3OvzBMPfsUO8LS8lUwKPW8MhERZEXj3crtSwAjbX5XBtyU1tuY23p\nzH5xxXYzX3h0q/L/ikIr+7asbKATzx2NibLWvBwTIpJTW3GcZE0URSbdAWwWAw5PkKFY3y2AU83D\nyt8OtxTwnGga4mLHGBc7pizwV9pGXpdE0iaKIh5/mMIMm4wvFWrAo7LqcIeli96iTwx4fnu4k98c\n6USMaUK2rSugvNA6ffVFQ4ztqGPAyeC4l90NRcqMhc1iID/HREe/U1nueqBv1I1Br1GKpVVUVGbH\noNhSJ6/hMcUV/8vSkmRype6YYUFlcTb+YISeIfe8mu8JgkCORY9znpK2jn6nEuwACYOteMwmHZ97\n9+Z5bVtl/ujVGp4Z9A67efpgR8Jrh873ExVFKouW7vm+nJTEsrkDY96Ea9DjDxOOiKwtzaFz0ElL\nr0MZT5yKM0BRnNxi9befeqiB/jEPfSMedtYVLdfHSEqyGp5gKEokKqoZHhWVufCEZgY8kWiUF0/0\nYM3Ss3ltPscuD3K+fSwh4IlEo4xO+vnOMxd59+1rF3QjuNA+yo9eauaOxlJcXmmQcdvmxBmkmrIc\n3r46zKjDT1HRzFmo1cikO0iu1ahq9VVU0mCqhie5S1tWXMCTynJYNiyoKslmJKbTn0+GByDbbGBg\nzDOvdZp7JhP+X5y3umZcbzY0goBBr1EDnjjk5+u9Oyq4c1sZT77WSlP3BMCM2p3rlbLY5MbAmIdt\n6wqU1zsHJIVIbraRBqOd45eH6B/zIooiQ+NejAYtgWAkwbpaAPZsLF41/bKSNR71+KXvdKXldtNZ\nHWdMRSUOb5KAp2/EQyAUYdu6Aj507zoEAc61jSrvX+ka54+/cZCv/fQMvSMennq9jUh0/rKBaFTk\nV4fa+T+/uMCEK8BLJ3s4fmUQm8XAprX2hGUrYjfj/tH5DUIWwvCkj/Y+R0brRqJRXJ6g6s6mopIm\nsqtW8j48YUxxBcPx9Rk/eP4Kr8V1WZctqauKrdy3q4Lf21etNBtNlxyzgWAoqgwG06H52rSAZ5YM\nj8ryYdRrVdOCOOTBcWmBmTVFVhprp2piK4tXTx3vQigrkK47eawgiiJvnOnl209fQAAaa/JpqJTG\nF01d45y6KsnZbo9Nsspyv4lYHd5qCXYgLsMTl0mWDRVWWm43ndVz1lRUYiTL8HTEZkJqy21kmw3U\nltto73MomvaD5/oJR0QcniAGvYZRh5/jlyVLy84BZ4I2djYCoQjfeOoczx3tpsBm4s6tpQRDUXyB\nCA/fVo1Wk3i5yP0rhibm3yE5E45eGuC/ff8E//tnZ+cM5sKRKF957G2ePdqlvOb0hBBZXY3AVFRW\nM7KkbXoX8Ug0SjAUTcjwaDQCep0GhyfIkYuD/PSVFrz+EKIo0j3kpjDXhNmkpzTfwiN31Ci1P+ly\nR2MpGkHgmz8/x5hj7n4doXCUtj4H5QUWpe9OcZ4a8Kw0Rr1WzfDE4fHJ2QBJ/tRQJQ38BQHK55kF\nXa0U5mah1QgMjHkJhiJ877krPP67FkwGHV/44Fa2rS9QPveVrglOt4yg02q4Y6sU8Lx4vJuvPn6a\n4Qkf9lXWPy+ZpE0OYlebpE0NeFRWHVMBz9TNrqNPCnhqyiTp2LZ1BYgiXOwYIxiKcKF9DJvFwKcf\nbuDLf7gLrUbguWPd+AJh/sePTvHX3z0+536PXhqkqXuCxtp8/vYTt/CBu9dhMemor8zl7h3lM5aX\nZ0uHJuYOphZCKBzhxy9d5fvPNREMRwmFo8oMymwMTfjoGnTxzKEOolFJEyynxdUMj4pKesg1F9Nr\neGTZ2vQeXEa9NqH4+q0LA4w7A7h9IaoWOFu9q76ID9+3nnBETChono3OASehcJT6SjtVxdloNQJF\nq6yI+GbEqNcmlT3eyLx+ppf/9ZPTnLo6THRazasn9iyzxHpAVRRZycsxUl2SPcOK+XpFp9VQnGem\nf9TDa6d7OX55iNqyHP7uE7coLq8FuVmU5pu52DFG34iHLTV5ygSFCLTFlB2rLuCJZZvCSTI8q03S\ntrqORuWm4fLYVc6PXObRDe9Gp0n8GXrCXnSCFoNGmh0Ydfi43DWOyaBVtLBba/P55ZvtnG8bI8ug\nIxCKcM/OcqXOZt+WEg6dH+Bnr7WmfUxyGvkP31mnzDb9rz+6VXJiSlLzUhTL8AynkT3KlHAkytef\nPEdbr4PKIivZZj2Xuybw+MOzdmWHRD//lmuT1FfZmVQbjqqozAujbFowTdLmk3vwGBMHZEa9NqHf\nxokrQ4pTUVXJwuU5t9QX8cSrLZxuHuGduytTLivL2eoqc7l3VwUTroCS6VFZOQx6bVITjBuV3mE3\nP3u1lUhUpLXXwZoiK++5s0apZZmq95CeuRpB4G9+fyfaVSTbWgxK86WA5+WTPWg1An/xga0zLOJ3\n1hXxXEyVsfP/t3ffcXKd5aHHf9Pb9l7VpVeyerMsVzlu2MbEBOwEE4PphBAScrmEC4FwuSSQED6E\nhE4gQAJJMN0mtjHGBlu25SLbataRtCq7Wkmr7WXaTrt/nDmzs7uzVbt7zsw+38/HH+9O21fzzsyZ\n5zzP+7yqOlMmm62i2Fr75+XO8EhJmxAZT7Y/w95z+3jp4sFx1wVjIQIuPzabjVg8wWf+Yz+9g1H2\nbG3MlIE0VAWoKvVy6FQ3h071ALB55chiwNuuWIrdZuOpA+fHPX4uA6FhtNY+VjSUZDY1A30TM+MN\nPZbP46Q04J7XkraH9rVy4mw/29dU89F7t9OcPktsHCQm0pkV8Dxz+AKQleEplpI2IabDyPCMLWkz\n9uDxjcnweMcEFBd7w5kObZea4QF9s1LVXMaJ9v5Mx6aJHGvV1/qsaS6jrsKfKZkR5vK47MTiyUzm\nvZCdPDfAF+5/hUQyxb23KHavr+XsxSH++UcHMgv2g+Hx2YCKEi+lgcI6Thlr9gZCMdYtLc+5QeoO\nVQ2Aw24b1dwgW3mJtU5Y5mpLHRoTxFqFBDzCFL0RPT37eNtT49o66wGPnsl55nAHvYNRbtjWxF17\nVmZuY7PpHwjhaIK9h87jsNtYlnUGtabcz67Lakc9bjwx8Vm1o2d6SaZSbFtTPaN/R225j+6ByLgv\nRHMhkUzyy6dPUxJw87bb1uJ2OTIHBeMgMZHsDcyePHCeR59vozddalMWsNYHphBWZbfZcDps487I\nhzMlbaMDnHG7oUfjmSYDc7UAe206cDnbOX6/H0M8keRE+wD1lX5KCuyLY75bLJuPJpJJvvqzQ/QN\nRbn7+lVcv7WRd92xnj++RQEj+1VlMjw5AoBCsmdrI470CdutE3zPaK4pYsuqKq7f1jjh+pdyi1Vo\n5O7SZs2SNgl4hCn6onrAc2awjVMDrZnLE8kE4XgYv8tHKpXi0RfacNht3HrFkhy7LetnQIZjSZpr\nisZ92XjtlUvJvouxODKXgfQO4zOtca+p8JNK6Wn7iYQiMcLRyQOUXILhOMPxJKsbSzMffsYZk9A0\nMzwf/ePtlBa5+c/HjvPMIT3bVWaxGmAhrMzldIwraTMyPNld2mB8AAR67X15sWfOAg/jMyo7izvu\nb57tIxpLsHaJZHWsxjhOFXqntpeOddE9EGHP1kZes2uk/NJ4/RoVB8FwDBsj+1gVKqfDzqffuYtb\ndy3hyg11OW9js9n4wBs3cc+NazKX3Xfr2lHZnokqTswyeZc2awWx1nrmxKIQS8QYigUJOPUFeY+3\nPcng8BA/Pv4AnWF9B+GAK8CRM720dwbZsbZmVJmZQS0py9Skr2wYv4N4fWWAj/7xdjan21wOThLw\nhNIBiW+GZyRWNep/9//+67McP9uX8zbv/6cn+auvPTOjxwUYCo8/82XUxAYnaFqQSqV48pVzHD/b\nj8ftYGVjCR9+01ZKAm66B/QDTKGVCggxn9wu+7h9eCLR3Bme7Jr7xuqRpitzUc5mMNYEdfVN3Knt\nUIv+OaqWyAbDVrNYMjy/2a+3Zb9xe9Ooy40uof3pioNgJI7f68y5TrbQ1Fb4uev6VTnX5kzk2s0N\nfOCNm/jEfTu4bksDW1bnLnUzixGoDmV9vwpGZR8eIUimknRF9DU3G6rW0VhUz8udh/jpiV/ym7Yn\n+Z9TjwIQcPp59Pk2AG7a0ZzzsZwOOxuWVwCwojH3xp8rG0szpSRDoUkCHuOMxAzPMl29qZ437llJ\n70CEv//+S5w4O3qPHKOMbigcm/G+QEaqP7vW1wh+JlrDc/L8AP/20FGGwjEqS7zYbDbqKwP87z/a\nQrHfRWmRu+DPpAkxl9xO+6hyDYDwBGt4jH17gFG7xM9FwwJD1TQyPIda9D3KZrrXj5h/IwFPYTcu\n6OgNU1Xqpb5ydGvp0nRJlpHhGYrELLfWw4qW1ZXw1testdQePKCfQPW6HVzoHmneZJTcS9MCsah9\n/cB3+fS+zwNQ7i1jT9PVJFNJ9l14EYAXL74CQCru5EBLN6saSzOtqHO57YqlbF9TPaphwVhGwDA0\njQzPTN+gdpuN265Yyofv3UkyleK5VztGXZ9dRtfRM7PmBsZ4RwU8U6zhyf4SlF0y0VhdxKfesYv/\n88fbZzQGIRY7j8uZKWEzTJThyf69uWYkyJnLDE+J34XbZZ8w4EmlUhw51UNthT/z5VJYh9utf+0q\n9AxPdDiRs8Qz4HXidNgzAU8oEs+0pBb5x2az0VAV4EJPKHNSNxSJ4bDbZpTJWggS8IgFdaj71czP\nZZ5SdtZuocg1fnOx/vP6mcmbdubO7hiW15fwp3+wcdJApcg/dcATnmWGx3D5+lpcTjtHW0eXtWX/\nzdaLgzN6zJGStpExTbWGJ3tDwruuXzXqutKAW/bhEGKGSovchKOJUXunjKzhmbhpQVNNVknbHGZ4\nbDYb1WU+OvvD4xq+AISjccLROPWyyaglGXs3jV3X+dLxzik77+WLVCpFNJbI2QbdZrNRVuTmQk+I\nf/+VRiyelAxPnmuoDJBIpriY7lhrlCmOXXdtNgl4xIKJJUZ/SS/3lOJyuLi2cTcA22o2AbCxYgMH\nD0JliYdtay69XtXIkLx8oosjp3ty3iazhmeWAY/L6WBlQwntnUOjgpzsn9smaWyQi5HFGZ3hMUra\ncmd4jHU6n3r75excWzOjvyeEGK98TAkOjGxE6nZOnOEpL9IbFZQE3Jl1C3OlutRHOJrI+TlgrFU0\nTvQIa6lMtxXOPjl1/Gwf//Ljg/zDD/abNaw5FU+kSCRTeCc4w19W5CEcTfD4/nag8Du0FbqGKv3k\nzrkuvawtFIlZrmEByMajYgF1R3pH/V7m0Rf8v2bZDawpX0lxqo6rGnbx+JNhhmNdvGbPUhz2S4/J\njYDhQEs3B1q6+fZHfm/cbUKROB6X45LqY9c0l3G0tY/jbX2ZtpNDWaVnbR0zC3hylbR5PQ5stonX\n8BgH0cpSa21OJkS+Mvat6huKZnY+N9b0ZK/ZgdFNC7xuJ++54zLsdtucn+msKtPf35194XH7eRhr\nFYvlS6QlGU0nOvtHShKNk2HzuafbQjLK9Tzu3F8xx54AiC+ijVgLUUOV/rl4rjvItlQVwUg8s9bQ\nSiTDIxZMV7oDm6HMqwc8DruDoc4SPvqNfRw55OD5V7tY2VjC9Vsb5+Tvjj3w52oRHYrGLnmBnUq3\ngDV2OIfRgcmxtr5M++vpyBXw2G02Al7XhBmenoEIfo9TGhMIMUeMDE92uZER8LgcEwc8Po+Ddcsq\nMp8Lc6l6ksYFkuGxtqpSY+5GMjxGx7JCYZR8TrSGo3hMp9CxpaEivxiNKc53BRmOJUkkU5ZrWAAS\n8IgFZHRn212/k9evun3U2p1X07uC/+o5fU+em3Y0Y7fPzVnRseny7NIUQygSn/X6HcPKhhKcDhta\n1joeI2jZuKKS4XiSR55rneju4xgND8bWN/u9zpwZnlQqRddARLI7QsyhskxJ28iXUqNNtcs1cVtq\n7wRnt+dCpjV1//jW1IMhfZy5dnIX5istcuNyjm460R8srDmLTrAxr8EI8EqL3Nx7i+KNe1blvJ3I\nD5WlXtxOO+e6giMbyRZKSZtSyg58BdgMRIF3app2Iuv6O4BPAHHg25qmfTN9+X5gIH2zU5qmve0S\nxi7yTHdYD3iubtzF2dMuvrjvFd5661rKijy0psu9jNr4XPvqzNbYD92egeioVpmpVIpQNE591fjm\nCTPhdjlYXl/CifZ+PYDyOjMBz2t2LaHt4iC/2d/Oa3Ytodg/dU1/rqYFoH+Q9AxE6RuKcqClm2s2\n1WOz2QhG4kSHE1Tm2LNICDE7xka908rwpD9r3C77nJ2wyaW6dKSkbSzjc6PYJ/ttWZHdZqOq1EtX\n1twZJ+Hmeq2XWYyW27maFgDs2drA/mOd3HuzYlu6/FvkL3t6+4tz3cHM508hZXjuBLyapu0GPgJ8\n3rhCKeUCvgDcDFwHvFspVauU8gI2TdP2pP+TYGeR6UoHPLGgl+89cpRXWrr53H++RP9QlLasDmal\nRW4qSuaunarNZsOR9eWjZ3D0WdHIcIJUavYd2rKpJeWkUmQ2ITXe/OXFHm69YinRWIJHnmub1mMN\nRWL4PM5x65gCXifxRJJfPHWK7zx0lJZ2/RyCrN8RYu6VF49vWpAJeJy5S9rG7s8z1ybbi8dYwyMl\nbdZVXeYjGIlnum0a8+iw2B4rsxWdoqRtw/JKvvq/rpNgp4A0VPmJxZOc6dC/y1lt01GYfcBzNfAw\ngKZpzwI7sq5bB5zQNK1X07Rh4CngWvRskF8p9Sul1G+UUldcwrhFHuoMd+G2u/nqT44RT6TYuKKS\n890hPv29FwhHR1q+rmoonfNFvl/4s6t5z+vWA4xr/Rme5R48uRg7mxvreIJZ63Cu29xAacDNY/vP\nTtoi2zAUjlGUY3+C0vRZwFfTpXMdvXpnlIMn9TVSjZeYqRJCjCjxu7HbbPTmCHjcEwQ8E5XyzBWP\ny0FpwD3pGp5iCXgsqzq9jqejN0wylcqUJg4XyN48EaNpwST7sFhtjxZxaYxObcbm636P9T5/Zhvw\nlADZW8onlFLOCa4bBEqBEPCPwC3Ae4HvZ91HFLhIPMKF4EXiQ0X0Dw1z156V/MVdm7h5Z3OmlfLq\nJr2MzQga5lKRz0VTeufzsQFP8BL34Mm2qqEUh31kHc9QOIYt/dhul0PP8gwn+NXzk6/lSaVSBMOx\nnDXdteV6R5SOHj3Q6ewLE4sneezFs/g8DnZdVnvJ/w4hhM5ut1Fa5KYvR0mbc2zAkw50vAvQNKSq\nzEvPQDSz2Z9BurRZX025HvD83b+/yKe/+0Lm9TQcK4xuZVOt4RGFpyG9TOBEu/7134oZntmOaADI\n3knNrmlafILrioE+4Bh65icFHFNKdQP1wKT1PeXlfpxOedNMprp67ja1my+HOtpJkWK4v5TbrlzO\nW+7YAMD7/3Arfr+bn/+uhfe+YTPR4QSXLa+Yl9R+oFgv9QpGE6Oes450wFVdEbik59K47+rmMo61\n9REo9hKJJSjyu6itLQHgDTeu4eHnWvn1C2fZuq6Oyy+ry/lYoUiMeCJFealv3JjWLKuE353M/D4Y\nifPq2X76g8O8fs8qljTNfVeofJEP74VCV4hzUFXm4/T5AaqqivTss82G02GntqZk1O0i6e+rpUWe\neX8emmtLaGkfwOZyUZ21yWgklsBut7GkqdxyG/8tRrleB6+7fjVx4JXjnZkz4gDxZLIg3j8uj15t\nUF15acfUuWKFMRS6DeifNee79ROx9bXFo553K8zBbAOevcAdwA/TpWkHs657FVitlKoAhtDL2f4R\neDuwEXifUqoBPRN0fqo/1Jsu1xG5VVcX09k5OPUNTfbimVcBSA2VcfX62lFjft3updyyvUk/O+pz\n0tMTnLdxeN0OLnQFR/39cx36GphUIjnr5zJ7HlbUl3D0TC/7XmmnfzCK3+Mc9bhvvnE1X//FEf7u\n357jM++5ItOmNNuTB84BUF/uGzcmn3P0l5i2C4Mcb+3DbrNx5bqavHg9zId8eS8UskKdgyKvk1g8\nyem2Xop8LoLhmN5pa8y/NTSklybZYd6fh+L0GVStpRN7oiJzee9AhJKAm66ume37JebeZO+HW3c2\nc+vOZsLROO1dQb7z0FG6+yMF8f7pSh/DhyMx0/89hfqZZDX2ZBKnw048oZ/1iUfjmed9IedgssBq\ntqfRfwpElFJPozco+KBS6h6l1Ls1TYsBfwk8AjyD3qWtHfgWUKaUegr4b+DtWVkhUeCeP6MBsLVx\ndWbzvmwTdXOZa+XFHnoHI6RSqcxlocjcreGB0et4gpH4uLK07aqGu69fSSKZ4tCpnnH3TyST/Or5\nNhx2W869iIxyCEPLuX7Odg6xY221NCwQYh6M7dQWSyTHNSwAfX3dmuYyNq+qnPcxjWxgOboJy1A4\nRkmgMLp9LQY+j5NVjaUEvE6GY4lRx6bZSKVStHcO8diLZ/nKzw7xsydPTn2nOWaUtC3UcV2Yz2G3\nU5f13c6KXdpmNSJN05Lo63CyHc26/gHggTH3GQbumc3fE/nttwfO0DHcji3p5y03bDZ1LEtqi9l3\npIOW9gFWpdcMhaJzt4YHoDm9VujkuQESyVTOFtQbVlQCxzlyupc9W0aCmt7BKF//+SHaO4PsXl9L\nRY4W0x6XIx246V++jOPjzTuXzMn4hRCjZW8+2lxTRDyeGNeSGvSD/kfevG1BxlRdNr41dTyRJBiJ\ns1wCnrzjdjlIAU8fukBDVYDl9SVT3mesVCrFp777AmcujJxNdzps3HnNijkc6dQyTQsk4FlUGqr8\nnO3UM8tW3IenMHogCkvIPjNl/Pz0ofN8/+VHsDnjXNO4y/SN1a7ZVA/Ab19uz1wWnuMMT4nfjc0G\np9MHHaOtbbbach+VJR5ePd1DMv1cHWjp5m++/RzHzvazXVXz5pvUhH/DOJNifOlZ3VTKioaZHyCF\nEFMb2XxUP8kwHE/idpl7+Kwe05o6mUrxpZ/o1eWN1UWmjUvMjtHx71u/fJUf/7ZlVo8xHEty5sIg\n1WVe7rt1LSsaSognUsTiC9v9LdO0QDqxLSoNWfsbWjHDIwGPuGRtF4f48k8P8t7P/5Yjp3voGYjw\noa88zef+8yW+9dABXPWn8Tl83LnuerOHytql5dSU+3ju6MXMjsChOWxLDemuTgF3pt11rs3kbDYb\n65ZWEIzEOXtxiPufOME/3f8KkeE4b75pDe+7c8Ok41ndVIrH5eDKDXoAd+uupXMydiHEeJm9eIyS\ntngyZ4ZnIZUVeXA6bHT26SVtF7pDHGjpZnl9CW+9/TJTxyZmLrtNs9E5dKaMY9ny+hKu3dyQ2YQ6\nNMvHm65EMskv9p7i5Dl9PWxUMjyLktGa2m6zWbJDn/VCMJE3uvrD/PdjJ3jxWGf6khT3P7+PHU2K\n3sEovYNRfEtbwRHn5mU34XWav77EbrNx3eYG7n+ihWcOXeDGHc0ja3jmsJVsWZGHvqFh/eccGR4Y\nWYvzyHNtPHP4AjVlPv7kzg0srZu6m8lrr1zGTTub8bgcXL6uhvpK2XtHiPlinLQwMjyxeO41PAvJ\nbrdRWeKlq1/P8BhfdtcuKaPY7yYSjE52d2Ex2RlDI0MyU8ZJNl/6WGb8PxSNU1o0d5t5j/XwvlZ+\n9uQpnjpwnr99166sttTyFXMxqU8HPH6v05IdIiXDI2btf55t5cVjnaxoKOEv7trMMhXmYuXj/OzI\n7wC4c08TnvpWilwBrm280uTRjrhqYz0Ou43fvnyOVCqVleGZu3K77DK2XCVtQGZh8bG2XgDe+ho1\nrWAHwOmwE/C6cDrsEuwIMc/Ks5oWJJMpEsmU6QEP6GVtg6EY4Wh8TjdQFgvPnbX9RmR44oxMa8cg\nX/7JwZybV4eHRwc8xmvBOMbNh97BKD9/6hQAXf0RfvV8W9bGo+a/R8TCqS334bDbLLkHD0jAIy5B\nz4BeSvGXd29h08pKmpbqGQ17YID1y8qx15wimoxy09I9eJ3zd3ZppkoCbratqaa9K8iJ9n5C6dI2\nn2fuUrBlWWfTyiY4s2YEPMbGqxNlgoQQ5vJ5nLiddvqGhjObRLossD+csY6nqz8y7uy+yC9uV3bA\nM3GGZ/+xTl481snzRy+Ouy7zGkiXExmvhfA8BjxnLgwST6S4ddcSiv0uHnz6DJ19YezpvarE4uF0\n2Lnr+lW89splZg8lJ3k1ilnrHxrG7bRnAoWgTd9szFcW5qYr6nm87SmKXUVc07jbzGHmtGdLAwC/\nffkcoWgcj9uBwz53b4fsdTsTZXhKx3RSKg1IwCOEFdlsNsqKPfQORYkljIDH/MNnVbppSVdfOHMW\nXwKe/DS2pG2i9tRGMHT0TO+468JR/bpMhscoaZvjNTwXe0P8848O0DsY5UKPvu6bX3gAACAASURB\nVFfiioZSXn/tCqKxBBd7w3jcDkuWNYn5dfPOZq7aWG/2MHIy/xNb5K2+YJTSIjc2m41UKsXZIX2z\nTIcvSNjTTiQR5dqm3Xgc1muRajQveP7oRbr6InO6fgdGsjpup33Cxy7JaledHTgKIaynvMjDYHCY\nSDqwcFsg4KkuHenUZnSblIAnP2WXtKXQO67lYpS7aa2944KisVk+/zxleB5/qZ2XT3TxSksXF9Kb\njNZV+Lh2U0NmWwYrLloXi5v5n9h5LhiJcer8gNnDWHDJZIqB4HBmIWRftJ+hmP7BF0lEeKxNX8ez\nvXaLaWOcjM1mY7uqJhZPEorG57zu3cjqlBV5JjzLVRIYWTNkBI5CCGsqK/aQYmSjT6cVAp5Ma+rI\nnO8nJhbW2DbnE63jMTI8A6EY57pDxBNJfvibE7R3Bcc3LZinNTyH0xtmDwSHudAdwmaDmnI/druN\nN92wGhjddU4IKzD/EzuPaa29fOJbz/H/vvsCrR2Do64bCA3z2Itn6ewLk0qlOHSym7aLQzkfJxpL\ncPhUzyXvsLyQBsMxUikoC7iJxKM83vYUAF6H/kW/feg8TUUN1PqrzRzmpKqyNvWcrwzPZOtyXE5H\nJqsj5WxCWJux+aix740lMjxGwNMfHmlaIAFPXnKPWRNmLPwfK3t9z9Ezvew9eJ6Hn2vlcz/YvyAZ\nnr6hKGc79ZObA8FhLvSEqCr1Zko81y4t5+7rV3HHVcvm7G8KMRfkk3EWkskUDz59mp/vPZXZ5f5o\nax9LaovpH4ry8HOtPP5SO8OxJKcv1OFyOnjipXZqyn189j3j17P84qlTPLSvlXtuXM2NO5oX+F8z\nO/1DUbAlGCx6lU8882OCsRA+p5cbl1zHAycfAeC6pqtMHuXkyucx4Kkq8xLwOlleP3nXtRK/m3A0\nTGmOvXqEENZhrMszAh4rrOHxe50EvE46+8KZzkhS0pafxmV4orkDnuzgRWvtpS7dpXMgFMtaw6MH\nT/OxhufI6Z7Mzx09IQZCMTbWjd70+jW7lszZ3xNirsgn4wz1Dkb55gOHOdraR0WJhzdcu5JvPniE\nE+39rFtazt/++wsMx5KUF3tIJIbRWvvoSpdAXOwNc7EvTE36rJzh4El9sf8vnz3DDdub8qK0qW9o\nGGfDSVodLfhSPm5bfhPXN12FzWbjfLCDbTWb2Vy93uxhTqoiK/sy1yVtXreTf/iTK6f8UlQScNPR\nG6ZMMjxCWJqRre3otU7AA1BV5uNcVzCT7ZGAJz+5x5SATVbS5nU78HmcHG3tozjd/MbncUy4D89c\nZniMcjaAE+16OX9thW+imwthGdb4xM4jX/nZQY629rF1dRWffNvlXLG+lpKAm5b2fp4+dJ7hWJLX\nX7Ocz75nNzXlvkywY5xpOZL1YWEwdlXuH9IDpHzQPxTFHtA/7D6+60Pcvvwm/C4/PqePt62/x/LB\nDkDFqAzP3O3BY/B5nFO25TRaU0uGRwhrKxtT0maFttSgl7XF4kl9LQXgleYneckzJoCOTljSFsfr\ndrB2SRlD4RgnzvYD+neMCffhmaMMTzKV4vDpXkoDbop8rswY6yv8c/L4QswnCXimkEqleODp03z9\nF4d57MWztLQPoJrLeP8fbKTI58Jms7GyoYTewShPvnIel9POa3YtweW0U5f1IXDTTr1U7fDp0QHP\nQHCY3sFo5gNq78HzC/ePuwR9wWFs7jBuu4dSz/Q2y7Sa7M2xfCZtlCUBjxD5wWhE0mlkeCyyx0h1\nqX7i5mJfGK/HiT0PKgTEeOMzPBOv4fF5nKgl5QCZtcFetzNrHx79eOZxO7BxaRmevQfP871HNL0T\n68UhBoLDrF9eMeqYVScBj8gD1vjEtrDjZ/v56e9Osu9IB99/9BgAm1ZVjio7u2xZBaB3QlnTXJY5\n81eb9SGwdXUVVaVeDp3qGXXmxmh2cMP2JqpKvbxwrHPCMztW0j8UxeYJU+YuM3sos5Y9h2Yt9DUa\nJxjtZYUQ1mSs4TE6XlmlpK06q0TaL9mdvDWTgMfrdrB2afmoy6OxBOFoAqfDnnlt2m02fB7nJXVp\n+9YvX+WJl9oZCA5nTtiuX14xaluFWgl4RB6wxie2hT28rxWALauqMpetTwc4huu2NGTOqq1pHgkA\njLMedpuN+soAV6yvJTqcYP+xzsxtTrTr6ehldcWZ61/Kut5qnnu1g/959gyt3d3YHAmqfRVT3ykP\nmPXlZc/WRt535wbUkvwNHIVYDFxOB0W+kdJXK3RpA6gpHwl4ZP1O/hr7esoV8CSSSWLxJF63k+pS\nLxUlI2s/w9E44Wh83H5uPo9zTtbwdPZFMut3LltWkdk42+NyTLi5thBWYo1PbIt6Ir251oqGEt7x\n2nV4XA5Ki9w0pTfWMjgddj79rl1cvbGe67Y0ZC43Ap7aCh8up50rN+i7zz6dLlu72BvikefaCHid\nrGkuY/f6Ov36wxcAiMUTfPxf92WCLiv42s8P86MnWmjp6gCgpqjS5BHNjWGTsmo+j5Mda2vyolGF\nEItdWVYZj1UyPMuyOmRJwJO/xm5KkatpgREEed0ObDYba5eMZHlC0TihaHzca8DvvbQMj+Fs1xDH\n2vpprimiNODOlGPXVvjk+CXygjU+sS3o1y+08b1HNIr9Lu67dS0Br4sPvWkLf/7GTTlrpOsq/Lz9\n9nWj0rwNVQFcTjsr6ksyt1nZWMKR0730DET45TNniMYSvPmmNRT5XNRXBlheX8zhUz30B4c52xmk\nvSvIDx8/sWD/7umyufU69kpvfmd4NizXx19dJiVlQojJZe+rZZWAx+91UpkujZXNHvNXTbmPHWtr\nuHFHE5A7w2O0qva69XnOrgxIpfQ1wWMDnoDXSTiaIBiJXdL49h48TzyRZH36mGkEPLJ+R+QLOR2U\nw8P7Wvnh4ycoDbj50Ju20lil97lf2VA6o8cp8rn45Nt2ZlK/AFdtqKelfYBnj3Rw6vwAbpedy9fV\nZq7fvb6OU+cH2XekI7MY1SpSqRR2m41kKoXDqwc8Fd7yKe5lbe97/QaOnulj86rCyFQJIeZP9pYC\nVunSBtBYHaB7IEL3QMTsoYhZsttsvO/ODbR3DvHrF84SzRXwpLM+3nRTgsvX1XKhO8SJ9n6Op7u1\n+dyjX5cbV1ZytLWPr/7sEGuayrjjqmXTzsgkksnMzy3pFtSZgMcvAY/IL9Y4RWUR0ViC7zx0lB8+\nfoLyYg9/9eZtmWBntuorA/i9I3Xfl6+rwemw87tXznGuK8SSmmLs9pEPn8svq8Vht/HMoQuZ/R6s\nIhSNk0ylWFpbzI6Neme2yjwPeLxuJ1tWV0lKXggxpa1rqjM/WyXDA3o1Aeh7vYn8ZgQzU5W0gZ7R\nu+v6VaPK7MdmeK7b3IgNOHK6l589dWpG3yvGtrP2eZysadKzShtWVLB+WTm7LqvNdVchLEcyPEBH\nb4iHnm3lWFsfF3pCNNcU8ad/sHHcBqFzwe91sXV1Fc8fvQjA0rrRLZ1L/G42LK/glZbuzIeaVQyG\n9JT4ktoihp36h2a5VxbbCyEWh7VZJUQOu3VOkty0o5mXjnXyhutWmj0UcYmMfZRylrSNCXgM2V1G\nx5Zn+71O7rlpTabL7EQbmuYydu3P9jXVmUC/rMjD//qjrdN+LCHMZp1TVCZ6YO9pfvfKOS70hLhx\nexN//Zbt8xLsGK7aWJf5eWnt+D1sdm/Qr9faRjYhzU4tm2UwNAxAsd/NwPAgTpsDv1PWvgghFgeH\n3Z4Jeqy0d1Z5sYfPvGc3O9bWmD0UcYmMdVi5A57RJW0Gf9Y+cnWV40vMbtjexGuvXAaQs1RuImMz\nPNuyMpxC5JtFn+GJxfU20T6Pg7979+5R623my/rlFZQE3AwEh8dleEBvge3zOAhHRz6YQpE4xX5z\nD7BGhqfY72JgeJBid7GUggkhFpW//MMtdPSGqS2XtQti7hn76JztHOJASzcbV1RkjrPTyfDUT7Cm\nxuPSz29HY9M/eWpkeJbXl7C0rpiNK/O7SZFY3BZ9hudASw+R4QR7tjYuSLAD+lnCN1y7gp1ra2io\nGv/h5HY52K5Gn6n73sMa//LjAzy8r5VT5wdMyfgYGZ6A18nA8CAl7vHBmhBCFDKnw37JazuFmMwN\n25oYCsX4p/tf4R//62VOX9AbBmQCnjHrdLLX7dRV5n5tGpmjmWzBEE5neHavr+Uttygc9kX/lVHk\nsUWf4dmb3hNn17qFXXh3zeYGrtncMOH1V22o46kD5zO/v5jejPSl410ANFUH+Phbd8zvIMcwMjwe\nH8STcYrdRVPcQwghhBAzcffvrWL3hjp+9EQLB09286nvvMDu9bWUBvS26OMyPFklbSV+F7kYAU90\nBgGPkeHJfnwh8tWifhV39YV55UQXy+tLWJJjLY2Z1jSX8Z7XrefY2T4e398OwNbVVexcW8PeQxc4\nfKqHpw9d4I31C9c0wAh47K4ogGR4hBBCiHnQXFPEB+/ezKune/jh4y08c7gjc93YgMfrGvkqN1GZ\nucc984DH2LvH78kdRAmRTxZ1fvLxl9tJAb+3rdHsoYxjs9nYdVntqKYGy+pLuGJ9He+4fR1Oh42H\n97WSTI7dn3n+DIb1kraUU9/rocQjAY8QQggxX9Ytq+Djb93BG65bQWN1gLVLymiuGV1dYWRgJivL\nd88mwxORDI8oHIv2VRyLJ3jylfMU+Vxcvs66nW0CWXv41JbrHdHKijxsWlnF/mOd9CzgRnND6QxP\n3J4OeCTDI4QQQswru93G7buXcfvuZTmvb6gK8MG7N09aqZIpaZtJlzYpaRMFZNG+ip8/epGhcIxb\ndy2x1I7ZYxX5cvfXryjRa3n7hqKUeuZv/A/va+VcdxCAMx2DuF12wgn9d1nDI4QQQphv44rKSa8f\naVow/YZHRtMCv2fRflUUBWTRvop/s78dG7Bnq/XK2bIFfCMZnprykYCnJN2iun8oiiPp4pUTXVy+\nrnZOd//u6gvzw8dPjLpsZUMJA8N6AwXJ8AghhBDWN9KWWjI8YnHKu1dxLJ4AbJf0xf70hQFOnhtg\n88rKcbsSW012SVv2zyWBkYDngHaRn/zuJK+e6eUdt6+bs71xTl8YBOD23Uu5ZlM9AOXFXv7r+I/0\nMUjAI4QQQljebLq0BSMx7DZb5r5C5LO8a1rw4a89w8e++ewlPcZvXtS7nl2/rWkuhjSvskvashWn\nW0/2DQ5zvjsEwNOHLvDwvlYAUqkUP/ndSU6dH5j13zYCnnVLy6kp91NT7udC+DxHe45jwyYBjxBC\nCJEH3DPs0pZIJjnbGaS63CcbjIuCMKsMj1LKDnwF2AxEgXdqmnYi6/o7gE8AceDbmqZ9c6r7TEfv\nYJT+oeHZDHnUYzx75AK15T42rLD+rsEup4O33baWmjGZqOyStot9IRx2GyUBNz96ooW6Sj+15X4e\nfPo03f1h3nXH+ln97TPpzc6W1umBTSKZ4BsHv0d/dIDbl9+M1+m5hH+ZEEIIIRaCxzmzgKe1Y4jo\ncIK1SxZu6wsh5tNsS9ruBLyapu1WSl0BfB74fQCllAv4ArATCAJ7lVK/AK6a6D6T6Yn0YU+fXXj+\nxEVw6R3COoM9s2o28ODzp4nbw1y7s4GB4dlnPxbSRqXvnNwX7c9clnJFwBWhY6iHjsEeKiqc3HvL\nMr70k4N84+H9vO6qZeCK0D88MOp+05VKpTjVdZHKKicxW4i+KBzsOkJPpJfrmq7k1uU3zNU/Twgh\nhBDzyJVewzM8zS5tWmsfAKpZAh5RGGYb8FwNPAygadqzSqkdWdetA05omtYLoJR6CrgW2D3JfSb0\n8af/btTvvq36/z+574lZDl1/jAf7nuDBvbN+CEvwbYX9AGthCPjq8YdxbNSve7BPv/4U8LG9P53d\nH7gMQsDH9j6Suchus3ND87WXNnAhhBBCLBi7zYbbZSc6zS5tWmsvAGpJ+XwOS4gFM9uApwTIThsk\nlFJOTdPiOa4bBEqnuM/EAxxsJhIdf5Odl9WOWsQ/Hd39EQ62dNFUU8Sqpvw/a/Hky+0kUylSKWis\nKWJ1UxnBSIznj3Rgt9tIJlP4vU4uv6xuxo89EBpm/9GLNNcWsbJx5Lm6rGYNa5csnct/RsGorpY1\nTWaTOTCfzIE1yDxYg5XmwedxEk+mphxTIpnieHs/9ZUB1qyoWqDRzR8rzcFiZYU5mG3AMwBkj96e\nFbiMva4Y6JviPhP64u//Gb2DUY6f7SMYjvFKSzcHWrq57sptrJ5h0PKNXxwmdrKDN1+9gxUNJTO6\nrxW99OjTdPXrJX67V6zmplXNDIaGefrBpzK3SfpdvOl118z4sV8+0cW+kwfYvmQlt64aHeB0dg5e\n2sALUHV1sTwvJpM5MJ/MgTXIPFiD1ebB5bATisSmHNOZC4OEInG2r6m21Phnw2pzsBgt5BxMFljN\ntkvbXuA2gPR6nINZ170KrFZKVSil3OjlbM9McZ9JlRd7uHxdLddva8rUkwYjU8ZK4xw61UNliYfl\n9eZHmnOhON24AKA2vUdPwOciu6FKMBwnlUrN+LEHgnpzCKP9tRBCCCHyl8flYHgaTQtGytnyvxJG\nCMNsA56fAhGl1NPoDQo+qJS6Ryn1bk3TYsBfAo+gBzrf1jStPdd9ZvOHjQ2wQpHYjO43FI4xFI7R\nWF1UMC0Ws7utLKvXM1Z2m21UIJRMpYhMc5FiNiPgyX4sIYQQQuQnt8sxrTU8WpvRsEDW74jCMauS\nNk3TksB7x1x8NOv6B4AHpnGfGTPW7cw0w3OxNwxAbbn/UodgGcvriznXFeTNN63JtKkGvWW1EbAA\nBMMxfJ7pT/X57iB9Q1EASiXDI4QQQuQ9j8tOPJEkmUxht+c+8ZtMpTjW1kdVqZfKUu8Cj1CI+TPb\nNTymGcnwzDTg0TfnrCn3TXHL/HHXnlXcsns5jeWjP5RKAi7oHPk9GIkz3WWHWmsvf/+Dl7IeSwIe\nIYQQIt95XCN78Ux0EvTsxSGCkThbVud/swIhss22pM00Ixme6Ze09Q9Fabs4BIysdSkEJQE3W1XN\nuBK9kjFlaDN5rlo7hkb9XuyfWSc8IYQQQliPxz315qNSziYKVcFneDr7wnzi288RTa9jKaQMz0TG\nZmVmUv7ndY9s5hrwOnE68i4mFkIIIcQYbtfUAc8xY8NRaVggCkzefZsNzDDg+cGjxzLBDrAoalLH\nZmWC4elneMJZz9VM1v0IIYQQwroyJW0TNDJKplJobX1UlHioWgTflcTikncBj9fjxMboLm3JZIpz\nXcFxt33peCevtHSPeuM67Hn3T54xo6TN6dBL3WZS0pb9vM6m9bcQQgghrKckfTK0eyCS8/pzXUGG\nwjFUc3nBdLMVwpB33/7tNhs+j5NgdOTL+Pd/fYy//td9nDo/kLksGkvwg0eP47Db+Iu7NnPjjibu\nvXmNGUNecMXpkrbqMr18byaBSziayPpZAh4hhBCiEKxsLAXgxNn+nNdrUs4mClhe1iz5vc5MSdvJ\ncwM8sb8dgGNtfSxP70fz4NOn6R6IcNsVS2moCnDPjYsj2AGoSQc6S+uKOd8dmllJW1aQ01QdmPOx\nCSGEEGLhrWgowW6zcexsX87rMw0LJOARBSgvA56Az8W5riDxRJJ/f0Qjlb78zIVBALr7Izy8r5XK\nEg93XLnMtHGapaEqwMfu3U5pkZtnD3fMKMMTSgc8b9yzkisuq52vIQohhBBiAXndTpbUFnH6/CDD\nsUSmiQFAKpXiWGsvZUXuzElTIQpJ3pW0ASyvKyYWT/L1XxzmTMcgu9fX4vM4OZUOeI6f7SORTHHT\nziWZNoyLzcrGUsqLPcDodTlTMTI8N+9spqJEFi0KIYQQhWJ1UxmJZGrUEgCAgeAwA6EYKxpKZf2O\nKEh5GfDsSmceXtQ68Xuc3P17q1lWV0xHT4gv/eQgB0/2ALC0tsjMYZrOYbfjcTsyWZvpCEXiuF12\naUcthBBCFJg1zfo6nuNj1vEMhPQTo2VFstm4KEx5+a12dXMZFSV69uIN162gNOBmaV0xAPuPdfLM\n4QsANFYv7oAHwO9xzqj5QDgal3bUQgghRAFa1aSvzxm7jmcwNAxAsV8CHlGY8jLgsdtsvOmGNdy8\ns5nrtjQCsGdrI1tWVWVuU17socjnmughFg2/xzntPYtAX8Pjl4BHCCGEKDilATe15T5a2vtJJlOZ\nywfSAU+JX743icKUlwEPwHZVzR/dsBq7Xa81rSnz8YE3bmLd0nIAmmskuwPg8zoJReOkUqkpb5tK\npQhLwCOEEEIUrNVNZYSjCc52DmUuGwzqJW2S4RGFKm8DnolsXFEJQJOUswF6hieVgsgEOytnG44n\nSSRTUtImhBBCFKjVOdbxZDI8AQl4RGEquIDnqo11XL6uhqs21pk9FEvwe/XgZTrreIzbGPcRQggh\nRGFZk17HczxrHc9gyMjwSEmbKEwF98222O/mvb+/wexhWIZRnhaKxqmY4rbGWh/J8AghhBCFqabc\nR4nfxbG2PlKpFDabTZoWiIJXcBkeMZqRrZlO4wIjwyMBjxBCCFGYbDYbq5vK6Bsaprs/AuglbQ67\nTSo8RMGSV3aB82VleKZi3EaaFgghhBCFa3VTKS8e6+SJl8/x5IFzDIZilBa5scumo6JASYanwBnB\nS3gaGZ7OvjAgixaFEEKIQrZtTTV2m43/efbMyPodnxz7ReGSgKfA+b36AsTpZHhePt4FwGXLyud1\nTEIIIYQwT1WZjyvW1466rGcgYtJohJh/EvAUOP80S9pCkTivnullSW0RVaW+hRiaEEIIIUxyx1XL\naKgKsHNtDQBOp3wlFIVLFmsUuExb6ilK2g6e7CaRTLFtdfVCDEsIIYQQJqot9/Ppd+4ikUxSW+Fn\n6+oqs4ckxLyRgKfAjWR4YpPe7qXjnQBsXSMBjxBCCLFYOOx2/uDaFWYPQ4h5JfnLApfp0jZJhicW\nT3KgpZvqMi9N1YGFGpoQQgghhBDzTgKeAmeUtA2FJ87wHG3tJTKcYOvqamzSklIIIYQQQhQQCXgK\nnNNhp67Cz6nzg8TiyZy3eemYXs62TcrZhBBCCCFEgZGAZxHYtLKSaCzBsba+cdclUyleOt5Fkc/F\nqsZSE0YnhBBCCCHE/JGAZxHYuLISgAMt3eOuO3VugP7gMFtWV2G3SzmbEEIIIYQoLBLwLAJrmsrw\nuB0caOkad50RBG1dJe0ohRBCCCFE4ZGAZxFwOe1ctrScjt4wHT2hUded7w4CsKy+xIyhCSGEEEII\nMa9mtQ+PUsoH/AdQAwwCb9U0rXPMbd4FvAeIA5/WNO1BpZQNOAscT9/sGU3T/s9sBy+mb/OqKl46\n3sWBk93cVOHPXH6hJ4zH5aCsyG3i6IQQQgghhJgfs9149E+Ag5qmfVIp9UfAXwN/blyplKoDPgDs\nALzAU0qpR4FmYL+maXdc2rDFTG1cMbKO56YdzYDesOBib4i6Sr+0oxZCCCGEEAVptgHP1cA/pH9+\nCPj4mOsvB/ZqmhYFokqpE8AmYAXQqJR6HAgDH9Q0TZvlGMQMlBd7aK4pQmvtzezJMxxLMBxPUlvu\nn+LeQgghhBBC5KcpAx6l1DuAD465uAPoT/88CIztZ1ySdX32bc4Dn9E07X6l1NXoZXE7J/v75eV+\nnE7HVMNc1Kqri6d1uys21nP/Y8f5wBefpNjv4k/esBmAFU1l034MMTF5Ds0nc2A+mQNrkHmwBpkH\n88kcmM8KczBlwKNp2reAb2VfppT6CWCMvhgYu8HLQNb12bc5gr6mB03TnlJKNSilbJqmpSb6+729\noYmuEugvos7OwWnddlX9yJQMhmJ8+f6XASj2Oqb9GCK3mcyDmB8yB+aTObAGmQdrkHkwn8yB+RZy\nDiYLrGbbpW0vcFv651uBJ8dc/xxwjVLKq5QqBdYBh4C/Af4CQCm1GWibLNgRc2tlw0gizuN2EIzE\nAaSkTQghhBBCFKzZruH5KvBdpdRTwDBwD4BS6i+BE5qm/UIp9c/ogZAd+JimaRGl1GeB/1BK3Y6e\n6bnvUv8BYvrsdhtvv20dPQMRGquL+M3+s5QXe1haZ36qUQghhBBCiPlgS6WsnWDp7By09gBNJula\na5B5MJ/MgflkDqxB5sEaZB7MJ3NgvgUuaZuw5bBsPCqEEEIIIYQoWBLwCCGEEEIIIQqWBDxCCCGE\nEEKIgiUBjxBCCCGEEKJgScAjhBBCCCGEKFgS8AghhBBCCCEKlgQ8QgghhBBCiIIlAY8QQgghhBCi\nYEnAI4QQQgghhChYEvAIIYQQQgghCpYEPEIIIYQQQoiCJQGPEEIIIYQQomBJwCOEEEIIIYQoWLZU\nKmX2GIQQQgghhBBiXkiGRwghhBBCCFGwJOARQgghhBBCFCwJeIQQQgghhBAFSwIeIYQQQgghRMGS\ngEcIIYQQQghRsCTgEUIIIYQQQhQsCXiEEEIIIYQQBUsCHiGEEEIIIUTBkoDH4pRSdqWU1+xxCFBK\n2ZRSLrPHsVgppRxKqbr0z/LZZQKllFMp9V6l1Eazx7KYyXHBGuSYYD45LpgvX44LTrMHICamlHo3\ncDPQppT6InBG07SUycNadJRSNqAC+BTwbeBFc0e0+Cil/MBnADfwJ5qmJU0e0qKjlLob+CCwAWgw\neTiLlhwXzCfHBGuQ44L58um4INGwRSmlLgN+H/groBd4L3CLqYNaZNIHNdJfJpYDdwPXKqUqTB3Y\nImE8/2lxYAWwQil1R/p6hykDW0TSmYSAUupB4E7gHcAPgTJzR7Y4yXHBXHJMMJ8cF8yXr8cFCXgs\nRClVqpQKpH+9DmjTNK0F+CpwEv2DtdK0AS4i6ec5kHXRNcB/AesAS6dtC0GO538J0AN8DrhDKVUD\nSCnJPErPQZGmaUHgw5qm3QOcA5qBdlMHt4jIccEa5JhgPjkumC+fjwsS8FjLp4H3p39+AP1AtkzT\ntE7g5fTlK0wZ2SKilPog8D/Ap5RS/zt98aOapv0ZcAa4QSnVZNoAC9yYdvJ2vAAAB35JREFU5//D\n6YuHgSeBw8AW4KdA05izfWKOjJ0DTdOOAGia1gcMAVeaOb5FRo4LJpNjgvnkuGC+fD8uSMBjEUqp\n64DfA65QSm3QNO0s+pv34wCapj0HrAI86dvLG3oeKKVWo5eIvA74AnCLUuptmqYdSt/ku+hnMrYp\npdwmDbNg5Xj+b1RK3YP+2n87+lntc8BFoFvWLsy9MXPwefQ5eGf6ukrgGDBo3ggXDzkumE+OCeaT\n44L5CuG4IAGPdSwB/hX4JXo9JMBngcuVUm9USi0HfKTnTN7Q86YGOASENE1rA/4G+JhSygmQ/sKx\nD71utd60URausc//p4BPAl5gP/C3wBuBo8AfmTTGQjd2Dv4v8BGllFPTtG6gHLgVpCvSApDjgvnk\nmGA+OS6YL++PC5Yc1GJgnInLemHcj14P/CJQrZR6jaZpg8CHgR3AD4Afa5r2OzPGW4jSi+6K0j8b\nZ0Z7gZVAg1LKpmnaXmAv8L6su/4b8K+app1Z0AEXmGk+/08BvwW2aZr2fk3TngeSwD9pmvZVUwZe\nQGb4HvhA+vpvAm9SSjmkK9LcyJ6H9O9yXDBBur3u2GOzHBMW0DTnQI4L82iG74O8OS5IwLOAlFKv\nVUp9M+t3u/HC0DQtomnaeeA48Bhwd/qF85CmaR8BrtI07TumDLwAKaXej/5FYlP6Ilv6TXwEPTX7\nJsBYCPwE0J2+n13TtKimaU8v8JALygyf/6eBU+n7OTVNS2qa1rHQYy40s3gPdABomvYCsFXTtMTC\njrgwjZ0HOS6YQyn1UeBfgNvTF8kxYYHNcA7kuDAPZvE+yJvjgi2Vkgz4QlFK/QXw98D2rPpfo067\nWNO0B9O/r0av0f6epmm/NmWwBUopVQ38Dv3M6efSZ0uzr9+OvvjxGqAF/YvGB4FPaZr2ywUebsGR\n5998MgfWMI15kOPCAlBKeYB/AGLA14FNmqb9OOt6eT/MM5kD8y2GOZCAZwEYZ+zSHS52AOWapt2W\nfoF9Dn3Dpj/XNO1g+vZOoEzTtC7zRl24lFI/An6B/ryXo6dq/wp9MeRW4F701pa70WtSv6Vp2m/M\nGW3hkefffDIH1jDFPGxCjgvzTun7tnwJfR+R16FvyN6OfnJS3g8LQObAfIthDiTgmSdKqfcAKU3T\nvpF+IXmAr2uadq9S6kX0dPjXgKNGaz8xP3LMxdvRN+z7OnrHo/9Grwf+qqZpF80baWGS5998MgfW\nIPNgDWPmYQnwUaAVvdPXQ4zMw5fS7b/FHJM5MN9imwNZwzN/rgU+qpTyp2safcAJpdS9gA3YDDxi\nBDtKdgeeT2Pn4jDwZeC76Tfx+4E70Dcwk7mYe/L8m0/mwBpkHqwhex5a0fcQeT1wKL0O5H3Aa9Ez\nbjIP80PmwHyLag4k4JkjSqm6rJ/XAwOABvxd+uJy9IPZNei9zPejly4AYOWFXvlmkrn4TPriF9H3\nTqhI/74UeEDTtDjIXFwqef7NJ3NgDTIP1jDJPPx9+uKvAeeBTekvdcuAx2Qe5o7MgfkW+xxISdsl\nUvruyp9E71H+APAroA+oQ69/PADcoWnaYaXUJk3TDqTvtwpYrmnao6YMvABNcy5u0zTtqFLqBvR6\n1Eb0dpaf1TTtcTPGXSjk+TefzIE1yDxYwzTn4bWaph1RSt0J3ACsAfzA/9M07VdmjLuQyByYT+ZA\nJxmeS3cfer3jn6NvOvYhIKHphoDvAJ8GyAp2nJqmnZBgZ87dx9RzYZxZ/S167fznNE27Rb5gzIn7\nkOffbPchc2AF9yHzYAX3MfU8/G36tj/XNO3PgE9omnZNoXzJs4D7kDkw233IHEiGZzaUUm8D9qC3\n5luOHgGfTGdt3g20a5r2xazbtwN/qmnaz8wYbyGTuTCXPP/mkzmwBpkHa5B5MJ/MgflkDsaTDM8M\nKaU+i96O74vojQfeCrwnffVZ4NfAUqVURdbd3oJeJynmkMyFueT5N5/MgTXIPFiDzIP5ZA7MJ3OQ\nmwQ8M1cKfEPTtP3oPcu/DNyjlNqiaVoEuAh4gSGllA1A07THNE171bQRFy6ZC3PJ828+mQNrkHmw\nBpkH88kcmE/mIAen2QPIJ0opO/ATYF/6oj9E3zTuIPBFpdS7gBuBSsChadqwKQNdBGQuzCXPv/lk\nDqxB5sEaZB7MJ3NgPpmDickanllSSpWgpwVfp2naBaXUx9Bbi9YCH9I07YKpA1xEZC7MJc+/+WQO\nrEHmwRpkHswnc2A+mYPRJMMze43oL6RSpdQ/A4eAj2iaFjN3WIuSzIW55Pk3n8yBNcg8WIPMg/lk\nDswnc5BFAp7Zuxb4CLAN+HdN075v8ngWM5kLc8nzbz6ZA2uQebAGmQfzyRyYT+YgiwQ8szcM/DXw\nj4upBtKiZC7MJc+/+WQOrEHmwRpkHswnc2A+mYMsEvDM3nc0TZMFUNYgc2Euef7NJ3NgDTIP1iDz\nYD6ZA/PJHGSRpgVCCCGEEEKIgiX78AghhBBCCCEKlgQ8QgghhBBCiIIlAY8QQgghhBCiYEnAI4QQ\nQgghhChYEvAIIYQQQgghCpYEPEIIIYQQQoiC9f8B/U/j4MyM/CQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1055a7a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gGwCQTqdNAP8zgC8BKAB4P51Ofy+bzS7v1QsmIiKi3ReeTDiuhMDmlClVbdh+\n5+NlfOeHmZ4vgDJzK0HxUo1A1e8P2e+G+frdIvGoBcsyNvskqjGyw9wvEcSPqr+u/l7V9QIJ6KbT\n2Ha6y2U3zJyZqEXblNZQWwYUQAeFy+npYfzeN7+4569nEOzV6PF+0k0BcxnAjwEgm82+lk6nvxTe\nkc1m/XQ6/Ww2m5XpdHoSgAmg0uJ5iIiIqA/Vn0xELBOe9OErDWEGDb9aa1iW0dU29K2uXp9v2jCf\nd7x9b5jfulvEMo2gD+awVy5dCiNYzaaxHeRFcf2pws1768gXKxBCIGJv9ukchlgUda+bAmYEwHrd\n7/10Om1ls1kJANXi5T8D8H8BuAKg2O7JxscTsOqayvpBKpU86JdAA4bvGeoV3zPUq/18z7z54yxs\nK4gSTYxGsbIe9MD4StUiRmPD0dpj3sou4ysvnu7qudcKlWpR1FjE+Erjsankvv45T06NYH65UPv9\n2HAEK+tu0GOiAI2jXciYpgFRHZOcK1b66t+tr6SStffcL7MP8B/f+AQLq0VMHxvCP3zxcbyQnjzY\nFzig+ul73ItuCpgNAPV/OiMsXkLZbPbP0+n0/wvguwD+SwD/stWTra1tnzF+kFKp5MCOkKODwfcM\n9YrvGerVfr9n7i1u1BrDbcvE6HAEBcdDRWpEbIFk3IZtGbUi5NPFfFevLzO3gpV1B+WKrDWTh6N7\nLdPAl9LH9/XP+eX0cXx/YaP2+/DPGrEMLKyWoBQgjMbpTUdFdbpucNpmGhgbivTtv1uPHYvjW19L\nN9zWr6+1n/X7f5vaFVfdFDA/B/DrAP6s2gPzXnhHOp0eAfDvAXw1m8266XS6COAI/rUnIiIaXPVb\nzwHUpk3lSxUkE5Emj++8dT2MpVmmUds9oZQGjKBn4WsXHt/3SFK7Xo7M3AquzN7BvaUipK92bdSt\nVx3VHE4T0wBUF9PE6kc7m6YAqs9pmkbT1xEucWx2H7B5sKTV5uS1+mLNNDbHRyfj9r406BPtVDcF\nzA8A/Go6nZ5F8H7/nXQ6/dsAhrPZ7J+k0+nvAfi7dDrtAbgO4E/37uUSERHRbgu3nm/1K194tGHc\nbv3jO7kye7e2MFKIIJ5kQGM4buNbX3/mwPopWvVy7GaPh9IajitRKkv41QLCVxrv31nFtcwC7iw0\nXzr50rkpjA0HSycFgFgk2N1idxG97/TT9HCM9d3FAkplD55UMA1RW3K5Vh2dfXKPFlMS7aaOBUw2\nm1UAvr3FJmVNAAAgAElEQVTl5g/r7v8TAH+yy6+LiIiI9km7k4n6kazdTp/KzK1gbmGj9mP/8Axg\nPBlFImYf2otj6SuUqosnw2lfxbKHNz94gNffb7508tLMNJ5/8nitv8gyBOKx4AQsjNvthqMwmYqO\nDi6yJCIiol09mbgyexe+H2y4rx/JnHc8nJoezKbhdiqej2JZwvX82m33l4u4llnAu7eWG/a4CAE8\nd/oYLs1M4/R0EkIICADRiIl41ELU7q9BR0T9iAUMERER7Zrw9EUACId6+b4GzOCE4rD0VmitUa74\nKJa9WoHiK4X376xhNrOAu13ExAxDIBG1EI+atWlvRNQZCxgiIiLaNVevzwf7VXTQHR42o2sNnJlO\nDnyMSSmNkitRcmWtQb7XmFjEMhCPWohFTIhdjIkRHRUsYIiIiOihhU3i73y8DIGgcDEMASNcjygw\n0MsGPRn0t5RdWZvo1VNMTADxiIVEzIJl8rSF6GGwgCEiIqId2ZxslUe+5CEZt2vb7aEBYQhoBHtF\nHksNDeTpi1uNiVWqO3A6TRN78dlJXHhuMyZmGQKJmIXYLjflEx1lLGCIiIioa82KlrzjQUqFtbyL\nRMyClKq2vyQ1lgAwWKcvWms4ro9S2YPcYUwsagcjkNmUT7T7WMAQEREdcWFRspRzkBqLbxuV3Klo\nUVrXThcqUmEsGUXB8eD7ClPj25+vX/lKoVQOxiCHSyZbxcSMakzsYl1MzBCbS0AZEyPaOyxgiIiI\njrDM3ErDEsvFNaf2+3BDffj7XL4CT/pwXAkg2N5uiGD7O0TQ4F52JaSvYJkGTk8n8e1vzOz7n6lX\nnvRRKkuUK34wNa3NNLFE1MKXt8bETIGhmM2mfKJ9wgKGiIjoCLt6fb7l7TNnJmr3O65EpW7PSf14\nZFEtXnylIURwp5QKuUIFmbmVvj19KVckSmVZ628pOB7e+rB5TOzERAIXz23GxMLdLUMxC7bFmBjR\nfmIBQ0REdIQt5ZwWt5cb7i84HoRAbcN8yPeDaWNKa2gAhhCwLAPJuI1Y1KoVQv1C6eCUqFiW8Ks5\nsZ5iYtXdLYmoBcPgaQvRQWABQ0REdISlxuJYXNtexKTGYg33S1/BEAJ+tYIJr92VRjAmWGuYZhAp\nC4sXYLMQOmi+UihW+1u07j0mxt0tRP2DBQwREdERdvn8iYYemPrb6+/fupzSMILel6hlYHoigaWc\nE4xPBpB3vFoBExZCB8WTPoplCbfa3xLGxF57fxEbnWJi3N1C1JdYwBARER1hYbwrmEJWRmos1jA1\nLPz/V2bvYG4hj0jErJ2wzK8UMTYcAQAMx23k8i4AQPqq9vxhIbTfHDfob/Gqr6WXmBh3txD1NxYw\nRERER9zMmYm2fSrh/ZvjloNCJ2abKFcb++PVE5eC40EABzI+WWkNp9rfEgwV6D4mFjblJ6IWItzd\nQtTXWMAQERFRV7YWOltHMIc7UF59+ey+Fi7Sr+5vqQT9LZ1iYpdmpnH+iSAmFjblx6MmTIMxMaJB\nwAKGiIiIdqRT/GyvuV6wv8WtngLdXy5iNrOA681iYmeO4eK5zZgYm/KJBhcLGCIiIurZZpzMQWos\njt/8B2f2pXDRWqNc8VEse5B+EBO7MbeGaze6iIkJIBYJRiDbFk9biAYVCxgiIiLqydbo2OKaU/v9\nXhUxSmmUXImSG/S3FBwPb37wAK9/0DkmZhkC8VgQb2NTPtHgYwFDREREPbl6fb7l7btdwHhSoeRK\nlF0JDeCz5SKuZeZx/dZKx2liUdtEImYhyqZ8okOFBQwRERH1ZCm3ffFlcPvuLa0sV4IxyBWpNmNi\nmQXcXdweE3vx2Um8WI2JGWJzmAB3txAdTixgiIiIjqitfSzdNuCnxuJYXNtexDzs0spwDHKpLOH3\nGBOzTSPY3cKmfKJDjwUMERHREfQwfSyXz59o+Nj623di6xjktjGxM8dwaWYap6aSMIRANGJiKGbB\nthgTIzoqWMAQEREdQQ/Tx7Jb45Pdio+SG4xBbhsTi1l48ZlgmtjocJS7W4iOOBYwRERER9DD9rFs\nXWrZLaU1ytWYmOwQE3tkIoGLjIkR0RYsYIiIiI6gvepjaUX6wTQxx+0cEzt35hguzZzA41PDMIRA\nLBJME2NMjIgAFjBERERH0m73sbTiej5K5fqY2CquZRa7joklohYMg6ctRLSJBQwREdERtFt9LM1o\nreG4PkquB+nXxcTeX8BGyWt47NaYWMQKY2K8RCGi5vivAxER0RG10z6WVnxVnSbmSigNfLZUwLUb\nC3j34xX4qk1MzBCIRYLTFttiUz4RtccChoiIiB5KxatOE6v4kNWY2GxmAZ8sFhoetzUmZhkC8Viw\ndNJgUz4RdYkFDBEREfVMa41yxUex3HtMLGqbSEQtRCNsyiei3rGAISIioq7tNCZmGgKxalO+ZTIm\nRkQ7xwKGiIiIOvKkj2J5MyaWub2KazdaxMSencKFZyeDmJgpkIjaiEe5u4WIdgcLGCIiImoqjImV\nyhKer1BwPLzxwSLeeH+xbUwsYhmIRoKYWMRmTIyIdhcLGCIioiMmM7dSHZ/sIDUW3zY+WSmNkitR\nciWU0ri3VMC1zAKu3+ocE0vEgtMW02BMjIj2BgsYIiKiIyAsWu4u5pEveUjGbcSiFhbXnNpCy/Rj\n4yi5EmVX9hQTs81wdwtjYkS091jAEBERHXKZuZVakZIveZBSYS3vYhxALGpBKY2/+cU9HB+NI1+q\n4M0PH7SMiV363Al87uwEIpaBWMREImbBthgTI6L9wwKGiIjokLt6fb72a+krAEF/y0apAss0oAHc\nXy7i3/1/H7eIiU3g0sx0EBMzDSSq08QMg6ctRLT/WMAQEREdcks5BwDguBJSKigNCAC+8lF0JYqO\nB08qPMiVax8zFLPw5bqYWMQKY2K8dCCig8V/hYiIiA651Fgcc/MbyOVdhGcmGoDWQC7vNjz2keND\nuDQzHcTEbAPxiIVEjLtbiKh/sIAhIiI6pLTWePujJSznHCznytAAWoW+YhET/8kLj+Lvfe7EZlN+\n1ILBpnwi6jMsYIiIiA6ZcAzyOx8v4UevfYJyxUfY1aK3PNYQwQmNaRq4v1zEsZEYotzdQkR9jAUM\nERHRIeFJVRuDvFGq4C9n72J5vQyltpYtgCkAIQDLMmHbJgwBrBc9Fi9E1PdYwBAREQ24ckWiVJao\nSIV7Dwq4dmP70kkgKFgMAfgKtQliIwkbZvXXqbHYvr92IqJesYAhIiIaAPWLKD2pYJkCJ1PD+MJT\nx3FqegQ35lYxm1nApw8al04aAkjEbAzFLHi+guNK+L6CZZm1ZZahy+dP7Pcfi4ioZyxgiIhoz4QX\n3Us5B6mxOC6fP4GZMxMH/bIGTriI0nEl1jY2Rx17nsJH99YhfQ3HlQ0fE4+YOPvoCFY3XAghYBgC\n0YiJZCKCV18+CwDV700ZqbEYvzdENDBYwBAR0Z6o3/4OAItrTu33vFDuzZXZu3iwVkLZ9QEEUTAN\nYK1Q2fbYWMTEcNyGbRnIFSr48rOTWFgpNS1U+H0gokHEAoaIiPZE/fb3rbfzwrk7Smv88uYD3J5f\nh1Z6c5LYlp58QwATozEYQsA0DRgGYAgBIQQWVkr49jdm9v21ExHtFRYwRES0J+q3vxccDxXPh1Ia\nnyzm8e7HfwtDCCgA0BqGEJBKQykNwxCwjL2/r6+fHxpCA1JpSH/7BLGQEMDoUASjQzYe5MqQvoJl\nGhhJRGq9LUu5csuPJyIaRCxgiIjaYA/HzqXG4rizkEcu70IpDVk3Ecv1VMuP85WGt4/3DcLztyIA\nFEoVFByvNipZa4W1vItxALGoxcliRHToGAf9AoiI+lXYw7G45kDpzR6OzNzKQb+0gXD5/AkUnOCS\nXG3NPNGusEwBXwO+rwERRMt8X0NpjXz1a8/JYkR02LCAISJqoV0PB3U2c2YCyYQNyzK2bX+nnRMA\nbFNgajwOyzKx2RgDmIaAEIBSGgLAqy+f5YkhER06LGCIiFoIezi2386egm6dmkoiNRZHPGrBEMHF\nN+2cIYIFlMdGYohFLUhfQVS/qBrBfZZpwLIMnDtzjMULER1KLGCIiFpIjcVb3M6egm6F8aXhuA1D\nsHzZKYEgLpYai+PJR0drDfqWadS+ruFXVykN39e4u5jHd36YYeSRiA4dNvETEbVw+fyJhj0m9bdT\nd8ITgKvX5yGEQKnswZMKWgdTt7RG7dfSr5vGZe79ffvx/Eqp2uN8XwfxrhZ5OkMERYoQQQxMiM3n\nty0Dp6aTeOXiKQCovS+H4zZyMhiIYJoGfKWgtMbIUASxiMXdO0R0KLGAISJqof7im9vKd27mzMSR\n+5p50kepLFGu+Pj0QQGzmQW8d3sFfl31YgiBmbPHcGlmGqemhhGP2UhELVimgVQqiaWlfNvPEb4v\nx4cjgBCoeArrRRe2adROaOofe9S+B0R0eLGAISJq4yhefNPOOa6E40qUXInM3CquZRbw6YNCw2OG\nYhZefG4KF56dwrFkFImYjXjUhOghYhe+L7eO+S65HmKR7f9pZ98WER0mLGCIiLbg7hfqhdIajitR\nLEusF1y88cEDvPH+Ym2McejR1BAunZvG556YwFDMRiJmIWqbO/684Zjv0OKag3zJAzS2ncCwb4uI\nDhMWMEREdZpdFLKHgJqRvkKpLOFUJD5d7D0m9rCajfMejtvIO962AoZ9W0R0mLCAISKq0273CwsY\nAgDXC/pbimWvdUwsbuPCs5N48dkpHBuJIhHtPSbWSbMx3/GoBSGCHTFLuTIilgAg8IO/u42r1+d5\nmkhEhwILGCKiOs0uCh1X4sbcKv75v3qTkbIjSmsNx/VRKntY6yImdv6JCQzFg9OWyEPExNpJjcWx\nuLb9/Xpqahjf/sYMTxOJ6NBiAUNEVGfrRaHjSuTyLizLgNK8CDxqfFWNibkSdxcLuNYpJjadDPpb\nohYMY2/33nQa883TRCI6rFjAEBHV2XpRWKj+hD1iGVjKOZC+gmUauDJ7hxeBh5gnfRTLEkXHw3td\nxMSOj8ZqTfm7GRNrp9OY77uLeeRLXu09m4zbiEUtTiQjooHHAoaIqE548Xdl9g7uLRVRdiVMw0DB\n8Wobz6VUmFvIIzO3wiLmENFao1wJ+ltW8+XOMbEnJ5CMR5CI7U5T/k60GvOdmVsJipfqkkspFdby\nLsYBnJpO7vOrJCLaXSxgiIiaKHsKx8fiWMo5KLsSWgMwUStiLNNgFOeQUEqjVN3dcnch3zEmdmY6\niaHqaYaxT6ctvbp6fR7DcRu5vNtwe97xOJGMiAYeCxgioi3qeweG4zacsgQQXOgaZnDBmozbjOIM\nOE8qlFyJQqmC926v4tqN7TGx4biNF6sxsdRY/KF3t+yXpZyDeHWUcsGpi5ElIiy6iWjgsYAhItqi\nfhJZvDpFypM+NADL2uwlOErLATNzK7VYHQCcTA3jlUunBvJiuFyRKJUlVjZax8ROpoZwcWYazz8x\ngWQigvgu7W7ZL+EwinjUqhUyADA1Hj/AV0VEtDtYwBARbbF1EtnocKQ2iSw1tnkBeHJyGN/5YQZL\nOafvxitn5laqzd0P/9oycyv405/cbIgjzc1v4Hs/uYlvfvXpvvkzt6OUhlMtXO60iYl97oljuHhu\nGmcfGdmT3S37pdOEMiKiQcYChohoi60Xf+FPsMeTUVQ8hdRYDCcnh/GL7FLtMf00Xnm3939cvT5f\nm8ZWL+94fd8HtDUmNpuZr50ihcKY2IXnqjGxPdzdsl86TSgjIhpkLGCIiLbo5uLvOz/MNP3Yfrig\n3+39H+H46K2kr/q2DyiMiS1vlPHG+4t484MHHWNiiZgF0xicmFgnrSaUERENOhYwRERNdLr4q++T\nabz94C/od/u1hZG6cCRvyDKNvuoDUlrDccOY2AZmMwvI3F5tOk3s731uGmdPjCIRsxCLDGZMjIjo\nqGIBQ0S0A1v7ZDZvP/gL+t1+bZfPn8Cdhfy2kbzJuN0XPRXSVyiVJfJOBe/d6hwTm6xOE7OtwY6J\nEREdVSxgiIh2oJ+bpHf7tc2cmcA//erTjVPIJofxysWDnULmVnwUy14tJvbGBw+29eqcTA3h0swJ\nnH9yAiOJCOJR81DFxIiIjiIWMEREVb1M7urnJun613Z3sQBP+rAto9Yb0+trDL8uFalw7swxnJwc\nxr0HBfzg727j6vX5ln/u3ZyEFlJao1yNic21iImZxubSScbEiIgOHxYwRETY2eSufm6SDl/X4k9v\nIxYJolKd/kxbC46Tk8O4cXsFcwv5YAli3MadhTze+WgZ48koYlGr5XPu9iQ0xsSIiCjEAoaICLs/\nuesghYXIjblVaKC2eDPU7M8UFhyOK1FwPHz6oIC3PnwA0zQgAEipsJZ3a6cYecdr+5y79fV0Kz5K\nrsTSutMyJvbY5DAunpuuxcQSUQuGwdMWIqLDigUMERH2Z6pYWFjcXczDkwq2ZeDUVHJXo2f1Jx+e\nrwANrKyXIYSArxS0Bj5ZzOPdWz8FtIYhBKTSkFJBAxAiiGAppaF1sEfFMgWMauHiSR+WaWwbq7z1\n6/QwX89uY2KfOzuBizNTeOKRICYWtRkTIyI6CjoWMOl02gDwxwCeB+AC+N1sNvtx3f2/BeC/AyAB\nvAfgv85ms9sXBhAR9bG9nipWf8LRMM1Lo/Z5d6OIqT/5sEwDlYoPqXTwieo+p1vxm3681oD0g8eG\npYBSGobZWBhYZmMj/Nav006+ntIPlk7mSxVcv7WCa5mFpjGxC89N4cJzk0iNJZCIWrAtNuUTER0l\n3ZzA/AaAWDabvZhOp18C8AcAvgEA6XQ6DuB/BPC5bDZbSqfT/xrArwH4i716wUREe2Gnk7taNarX\n335yagQPVoIL8a3xpzCKtRtRtczcCm7MrcLzFSzTQMQyUHbljp8vLHmUDk5FDCFgWwa0DmJp9bZ+\nnXr5elY8H8VydzGx55+cwMhQBPGoVTsVIiKio6WbAuYygB8DQDabfS2dTn+p7j4XwKVsNluqe76D\n3+JGRNSjnUwV29qofmchj8zcKiK2gYqnar0n88sF3JnfgGkYqEgfAoBhBLGsMIp1d7GA7/wws6OJ\nXZm5FfzZ33yE+8slKB3EwFQ1FiaECI5VHoJA8HwwgCceGcW5M8dw4/YK7i0VIX0FQwj8n99/Lzip\nMQQsQ0ABgNaI2CaGYjYenxpu+DNprVGujkGemw9iYu/dWoXSzWNiTz46hkTUQjTCpnwioqOumwJm\nBMB63e/9dDptZbNZWY2KLQJAOp3+bwEMA/jrdk82Pp6A1WdTYVKp5EG/BBowfM8cTl9JJfGVF093\nfNwvsw/wH974BG9nHwAARoYiADTWCxUAQMHzYBoGcoUKJkwDnvShFKCUgoCAhob0NYTQEBBYXA1+\nBrSasGGaBlbzLv7i53cwOprAC+nJjq/l3/zHj7Cw4tROTLQGfF9DWAJCBNvnTVPA93VDgdCJaQho\nDZimQNQ2cfrECP6n/+Yyfpl9gOu3VjA6HMHSWhmuvxlH85WGh6DosaxqIZcQeOXvP4EX0pPwlUbR\n8ZDLu3jro2X8zS/u4e78RsPnHRmK4O9//lG8/MKjeOT4MIbi9rbI2lHAf2eoV3zPUK8G9T3TTQGz\nAaD+T2dks9laJqHaI/O/AHgawKvZbLbtfx3X1krt7t53qVQSS0v5g34ZNED4njmcut1ZUn/qUpE+\noIHlnAMhBHS1OPCVhiGCX+cKLgwhYAjA1xqmEJDVLsGgOAgiVIYhkC9WGiZ7XfnZLTx2LN72dV/5\n2S3kCpXa5xYIol8agO8HgwKG4xZKZQlDBHGwVgQAYVRfl2EgYhsNE8yKjoelpTyu/OwWPKmQK1Tg\nq+Ytj+HnF6aBXMHFv//pxxiJGHiQc/B6u5jYzDQ+/8QERoaiiEVNVJwKKk6l7dfgMOK/M9Qrvmeo\nV/3+nmlXXHVTwPwcwK8D+LNqD8x7W+7/FwiiZL/B5n0i6nfNChUAXe8s2dokL6vVSDidSykN6M0J\nYF41IiYAWIYB2zbgu7KW6orYJtyKD6U0lnIObMuAUoCvFD5ZyOO9Wz+FAmAZAidTw3jl0qmG17SU\ncyB9hfqkmKj+H8sy8PyTx3H5/Alcmb2De0tFmL6CUT1Z0TqIfElfQykdTEWbTgIaKHvbm/zDBvxw\nwpj0Vdt0mqp+jooncXcxj3/5ow+Rud0qJjaNJx/dnCZGRETUSjcFzA8A/Go6nZ5F8N/F30mn07+N\nIC72FoD/CsDPAPxNOp0GgD/MZrM/2KPXS0S0Y62WK8ZaXDA3a6yvHw88HLcbJooppeErDWEAtYOJ\nak+K1oCGRjJuB30vOigwbMtAqbzZaO96jT8HcqrTwioCuPlpDnf+PI9XLp3Cr108DWBz2pfyNfy6\nwkAgKLDCk6ReBgRs/TqFwmIv/JxhAde2iFEaCsH45PoRyuE0sZeem0RqPJgmdhRjYkRE1LuOBUz1\nVOXbW27+sO7X/C8OEQ2EK7N3N08sAKDaj+L7GsdHYw3xLaD5zpL68cDx6uMLjgchgvHDZnVniqcU\nGq7rRRAtW14vwzINaGhELAMbxe7iUWGR4Ho+/vynt/FX1+7i+GhwIlLxfCgdFE7QwWNty8TXLjy+\no8lmnQYahBPGhuM2KhW/bV+Nv+WuWkzsyQmMJKKIR7m7hYiIesNFlkR0JGTmVjC3sAHozZMSIGhQ\n11pjLe9iHGgoYprtLDk5OYzM3CpkdVRxMm4jNRbHqy+fxff++ibyJa8W6TINAejN3helwyZ6jUTM\nQqV6ehH2rfTCrfj4bKkI0xQYjttwXAlPKkRsE6emk3jl4qmHGsvc7tSmvsABgFLZg+PK4NSpydAz\nQwDnnzjOmBgREe0KFjBEdCRcvT5fizzVnxiEvR9ab+5kCW3dWZKZW8EvsksYjtsoOEGhknc8PHly\nFFevz6NQCprSx4ejyDsepFSQ1cljhiFgQMCyDKTG4ojZBu4tFREePpiGCOJWXVYy4cOkr5EvebUT\npKnxOL79jZkdf5269dypYzg9PYKNUgXvfLSMazcW8NlSsaESS8ZtvFiNiU2OJ5CIWTANHtoTEdHD\nYQFDREfCUs6p9ayE9YtGdeSw0jANUZ0eJlrugAlPHOJRqxYfc1yJtz9aRmosjqHq86/lXSRiVq0/\nxLI2I1LhAsiK1Dh35hjuLOQ3+2gEasWVUd9H04GqO0FqFnvbTZ5UKJU9PFhz8NoHwTSxYoulk59/\nagKjQ1HEIoyJERHR7mEBQ0RHQmosDlXtXVlZL9ciZOGeFK0ByxT4zX9wpmV0qr6BPxSexACNPTGe\nVDhzIon7KyVUpIJpiIaRxGGRFPbThM8TNQWSCRuO6wdTvhAUDe0a5cPSIO94wRSxPVCuSBQdD7fu\nB0snW00TuzQzjSdPjiIRtRBhTIyIiPYACxgiOhLCxvN41MLEaAzLOae67ySMbgX/++6PPsS3vv5M\n0yKmvoE/FPbChMLTGUMI/N43v4jM3Ar+4ud34MnG45T6E55WzfKhzNwKrszewa37G01jZoYhaq9l\na+ztYSit4bgSG8UK3q6PidWpj4lNHRtCPGoyJkZERHuKBQwRHQlbi4UNu1Jbtqiq+1AMIVBwvJY7\nYMIiqF7YyL9VOABg5swERkcTuPKzW02LlG5GHIePCQuZjz/bgNYapmkEp0fQsEwDj6WGHqpxPyR9\nhVJZYnGthNfebxMTm5nGF548jpGhCGNiRES0b1jAENGRUV8sfOeHGSyuOcFY5brTkfA0pdkOmGYn\nJl9Mp/CL7NK2z1V/EvJCehKPHYvv2utvtafllUunH+r5e4mJPXUymCZmW4yJERHR/mIBQ0RHUnia\nEvavhMLTlFbN8M1OTE5PJzvGwHZTt9GzbiilUXLl9mlidWoxsXNTmKounQxja0RERPuNBQwRHUnh\nxf53f/QhCo5Xi4LVN9n38lx7WbDsxef0pI9SWXacJnZpZhqfZ0yMiIj6CAsYIjqyZs5M4Ftff6Zp\nHGs3m+H7hdYa5Ypfi4ldu9E8Jnb+iQlcPMeYGBER9ScWMER0pO1mHKtf+UrBcX1sFF288/EKrmUW\n8NkyY2JERDSYWMAQ0ZF3EBGw/eBJH8VqTOz19xfxxgeLKJZlw2MYEyMiokHDAoaIDq1w7PC9alP6\nydQwXrl06lAWK6GtMbHZzAJuzHGaGBERHR4sYIjo0Nm6+DHc8TI3v4Hv/eQmvvnVpw9dEeOrYHfL\nRrGCd2+1iIklbLz47GZMjEsniYhoELGAIaJDJdyRspRzoJSG1oDva8AEDCGQd7ymO14GVcXzUXLb\nx8QenxrGxXOMiRER0eHAAoaIDpWr1+cBBNvk61JTwUmMKSB91XLHy6DQWsNxfRTLFdy+n28ZE+M0\nMSIiOoxYwBDRobKUcwAAlmlAys0iJry0t0yjpx0v/SSMieVLldbTxBI2Ljw3hQvPcZoYEREdTixg\niGggZeZWqqOPHaTG4rXRx6mxOBbXHAzHbVQqPvxqBRNewifj9sDteHG96tLJnIM3OsTEvvDUcSQT\njIkREdHhxQKGiAZO2OcSWlxzar+/fP4Evv/T24hHLRwbjWG94MKTChHbxKnpJF65OBhTyHqOiT02\nhkTUgm2xKZ+IiA43FjBENHDCPpdmt3/7GzO1Xy/lyjg9nRyoxZTSVyi5EvnqNLHZzALut4iJvfTc\nFCYZEyMioiOGBQwRDZywz2X77UFz/tbFlJm5FXznh5ltcbN+4laq08RywTSxNzvExIJpYvwnnIiI\njh7+14+IBk7Y51LPcSWkr/DP/9WbDUVKu7jZQRcxSms4rkTJ8XB7njExIiKibrCAIaKBE/a5AEHh\nsl5wUZEKEcuEUhqLaw7e+XgZZ6ZHWj7HQe6CkX516WSpgusdYmKcJkZERNSIBQwRDZyw8Lgyewfz\nKy58X8M0BHxfYb3gwzQFDCHw6VIB0lcYH44iFm38526/d8ForVGu+HBciaX1MqeJERER7RALGCIa\nSFhZy2QAACAASURBVDNnJnD1+jxOTCh8tlSAUhqqmrzy65ZWWqaBvONtK2D2axeMUhobxQoe5Bzc\nmc9jNjOPG3NrTWNil2am8eSjo0jEbMbEiIiIWmABQ0QDaynnwHEllNKoqwegddBfErFMDMdt5Aru\nto/d610wnvRRLEsUSh5u3VjEX79+d1tMbCRh48XnpvDSuWlMjsUZEyMiIuoCCxgiGjjhEssHaw7K\nFT/YUqkbH6OURjJuIxa1MD4cwehwFEu5MlJjsT2bQhbGxEplieWNMl6vxsRKTWJil2am8YUnj2OY\nMTEiIqKesIAhooFSP1VsKG7XigNhAFBBHSMEYBhGLTb2yqXTe9qw7ysVLJ10KrizUKjGxFZrkTYg\niIk9/+QELp2bxhOPjiIRs2Bb5p69JiIiosOKBQwR9a3wpCXc33Jychh/+/ZnKDgeLNNAMm4jYpvw\npA9oIB6zELEMVKSCADA1vrc7X+pjYu/eWsa1zALur5QaHjOSsPErX3wM588eY0yMiIhoF7CAIaK+\n9JfX7uDHr39Sa8Rfy7t456NlKK1hCAEpFdbyLhIxC1prQAT7YUKvvnx272Ni68HSyTc+fNA0Jnbx\nXBATO3PqGAobDmNiREREu4AFDBH1nczcSlC8SAUAkFKh7EoYhgia9evqgIpUGEtGIX0FQ4g963Hx\nVbC7pVT2MLeQx7Xa0snNx9THxM4+OopE1ELENpGI2Sjm93dsMxER0WHFAoaI+s7V6/OQvmq4Teug\nMX9r/Er6CvGotWcnLhXPR8ntHBO78Nw0Ljw3idRYHImYBdPgGGQiIqK9wAKGiPrOUs6BZRq1Exil\nNDSCIkZoIBG3UJEK0lcYjtu7XrxoreG4Pkquh5X1ctuY2KWZaTz/xASSiSjiUU4TIyIi2mssYIio\n76TG4iiWJXJ5F0pp+HU5LSGAUlliPBlFbJdPXqSvUHKDmNidDjGxi+emcfaRYJpY1OY0MSIiov3C\nAoaI+s7l8yewuOYAAFbWy4AADADxqAWNoNDwfIVv7lLx4laqMTHHw/UOMbEXw5hY1IJlMiZGRES0\n31jAEFFfCUcnlyuy1pgfjZi18cjhVDLbMh+qeFFKo+RKOK7E6kbrmNipqSQuzkzh+bMTSA5FEYua\nMBgTIyIiOjAsYIiob9QvqYxFLMQiQLniI2IZDYWFlAr5UgWZuZWeixhPKpTKHhxX4s5iHrOZBbzP\nmBgREdHAYAFDRH3j6vX5bbcNx22srJdhbpk+lozbuHp9vqsCpn53S8mVjIkRERENMBYw/3979xoc\n133ed/x7Lrt7dsElAZJLArIupCzpSCYMWZZsCTRtS504cat4nMTtmzS9OHVbdzrTdtqZXpO+adpO\np0k6k3Q8ubSp06TJNInrpInGjpM0lk2RcizZFgXaOrqR1g0AQRAAAezu2T2Xvji7y11gF1iAIIDd\n/X1mOMKes9g9Sx1R+PH/PM9fRPaNucXSumPZjI1hGNi22Sgfy2dTOBmbucWN91YJo6g2TSxgYdMy\nsVEevPsw+aE0TsZWmZiIiMg+pQAjIvtGYTjbaN5vdnAoRT6XbvN8p+3r1PduKXdTJjY+xt1jB8ll\nbDJplYmJiIjsdwowIrJv3H7sAFOXrq1baXn8oXfxvDe37vlnJsYaX0dxTLm2d0vJDzuXiQ2lefSB\n40mZ2KFk00mViYmIiPQOBRgR2RemLs3zvDfHgWyKlVKVIIxYLlX50MQYPzx5ghOjec5emGZusUxh\n2OHMxBjjJ48ke7eUA0qVZN+YjmVio3kmT40yUSsTy6pMTEREpCcpwIjIvlBv4M9mbLKZG380vXVl\nBYDxk0daGvbLlWT8sV8Nk00nL64vE7Mtg4l3H2VyfJSTo3lyjo2T1h97IiIivUz/JxeRXVXf52Vu\nsURhONtYSWnXwA+0NOrX924p+gF+JSkTOzc1w3SbMrHH3nOcD9x/jKO1aWIpW2ViIiIi/UABRkR2\nTfM+LwCzC6XG404N/IVhh2qQjEAuV0IWVpIysW9+7wpFf32Z2OnxUcZPHiafS5PL2JimysRERET6\niQKMiNy0Tqsqa9XLxEp+0OhzsS2Tp85d5snTJ1rCTRzHxDFMvPsIV5fKSZnY1Azfvdy5TOzE8XqZ\nmIWh/hYREZG+pAAjIjdlo1WVtSFmbrFEyU+a7euCIOLSzDIAn/ro3XzthXe4cq3EcD7Ng/cc5dp1\nn6fOv9i2TOzRB47zwQeOcfSQQ86xSdkagywiItLvFGBEZJ36isr3Z5epBhEp2+Su4/m2Kyv1VZW1\nzl6YZvzkkZbVmaXVCqulYN1zbcvk6e+8w4//wH382EfezWKtTOwLX329c5nYiRHyQxmyGQvLVH+L\niIjIoFCAEZEW9RWV+kpJFMVEcczcYonnXrqCZZmkbZPbCwd48vRdGzbfr12dsS2TSjXEsgxMwyAM\nI6IYwiji4uvz/Onzb/Lm7ErbMrEHa2Vidx4/wJCTUpmYiIjIgFKAEZEW9RWVlVKVKIoJo5gYoBYo\noiAijmNee3uJX/y9FwnjGOKYlG0xfCCNUxuBXBh2WlZn6n0vGBCEMYYRQwyGAVEE5SjiS8++0XIt\n9Wlij9x/jCMHHYYcm3RKZWIiIiKDTAFGRFrUV1SCMCKK47bPCcL68eSfBlCphsxfL3PkoIOTsTkz\nMcYXv5asvpT8gIXryThkEwiBOE6+L2rzFmnb5PR7R/nYw7czVJsmZlsqExMREREFGBFZI21bvDm3\nQhBEbcNFO4YJxMk+LdUw4kNugbMXprmyUCKKY8Ja4IlrqcUgCTDtXt40kpHK11crjB4ZUpmYiIiI\ntFCAEZGGqUvzLKz4BEGEaRgdV2DWiZP+FgxI2RbPe3NEUYyTsbm+4lMNYwwjCS2bvlScrP4sLFcU\nXkRERGQdBRgZeN3uYTIIzl6YJlvrYVkpVYkqAVGUnDNov2JC03HLNPArAZZpUK4ErJaqjXKzteHF\ntgzCMG55TQOwLIPlUpW7RvM79KlERESknyjAyEDbyh4mg6De/5LN2I0gU/KTIHIgl2Z+qUQYJbVf\n9eBhQPI4jrEti9VyleurlY7lZ4YBlgGHhtIEUUypXE1WaADTTKaTBWHEmYmxW/1xRUREpAcpwMhA\n22wPk0FTGM4yu9A6FjmbsTkxmueznxxvCXzFcpWlFZ9qEJNKmQwPpVktBwRh3LFUzCBZebEtkyMH\nMwznHWYXSo0JZUEYYVsmdxSGBvL3X0RERDanACMDbaM9TAbRmYmxlhWp5uOQrEqFYcTXXpgmDGPc\nO3Pcf9cIVxZKnL0wnazOdGCZyQrLbUcPAFANb7xf84oPwJOnT+zsBxMREZG+oQAjA63dikNy3NmD\nq9l79VWPpCeoTGHY4czEGKdOHKbkB5T8gOOHh/irj7+byzPLnJua4QtffW1dudjahn3LBMs0se0b\no5ALw07H99Pqi4iIiHSiACMDbbMVh0E0fvJII0CEUUTJD5lbKicjkoOIF169yvmLM0zPF1u+L2WZ\nOBmLnJMiCEKur1aAZNWlHmby2VTj+c2rOgosIiIi0i0FGBloWgFor1INKfoBfiUkBhZXfJ69OMs3\nX7pCyQ9anntiNM/k+ChXFos88+IM166XSNkWQ9kUlSDiYC7NoaEUGAaVaqTfYxEREbkpCjAy8LQC\nkIjimLIfUvSrtUb8mEvTy5y/OMN3L19rKQmzLYMH7znK5KlR3nV0iMsz13nmxSUO5tKNZnyAjz96\nJz88eWJvPpCIiIj0JQUYkQEXhBHFcsALr13luZeuMH+9jAEU/ZCFZb/luYeG0jz6nuN84IFj5LMp\nck6KXMbmi19/HcMw1jXjv3VlZZc/jYiIiPQ7BRiRAVWuBBTLAZUg4pW3Fnnq/PcplgNWy9V1Y5Dr\nZWLvOXGYTMpkyEnhpC0MwwA0zU1ERER2jwKMDLSpS/O1/pcSadsEDCpBSGE425d9GlEUU/QDin5A\nFN0oE/vff/YqxTW9LQAj+TR//WMutx0dIpOyGHJs0ilr3fM0zU1ERER2iwKMDKzmTRlLfsDbtXKp\nkXyG2YVS41w/hJhqEFIsB5RrTfmVIOSFV+c5PzXDzLXiuuebRlIuNuSkuef2Q+QyNrZlrn/hGk1z\nExERkd2iACMD6+yF6cbXK6Vq4+vlUhWn1sdx9sJ0zwaYOI4pNTXlAyws+3zjuzN886W5ddPE6kwD\nLNOgXA255/YsB3PpTd9L09xERERktyjAyMBq7tuoT81a+3Uv9HA0l8EVhrNMjo9ycuwgZT8girkx\nTWxqhu9+/9qaDSYNojjGiKH+qaMYTCCMYj784G1dX4emuYmIiMhuUICRgdXct2FbJkEQEUUxURzz\n5uwycQyGCf/g55+GOMY0DIJa34hhGNimkfzQ33QuimJMc/25bp+31XNRGBFGYJqQsk2Wi1Xemlvl\nhz54B3eN5juWiVmmwfvuPUqxXOXS9DJhGGFEMXHydsQxnBzNK5CIiIjIvqMAIwOruW/jQDbFtaUy\nQdQ6fiuOwK+Ebb47ptrmKCQrF53Odfu8rZ6LQgijkLIfYhjwW3/yMgB+NWp5nkESdgxiLk9fxzQN\n8rkUSysVLMtoeeKTp0908SlEREREdpcCjAystX0bpXLAqh8Qx/G6McK9oH7NcdwaXAwDTMPAIPlc\nUQQxST/MkJPi8CEHwzAaG1DalskdhSGtvoiIiMi+pAAjA625b+Pf/fo38a+uQmxQDSJ6MMOsk7JN\nIKYa3Pg0Ru1XFCfDC0qVgHTKIp9NNYYXaPVFRERE9qvOc1FFBkxhONsYFWwYmzy5R1SDiCBYUxZX\n+1X/OopiwjBmYcXHSVt86qN3a/VFRERE9i0FGJGaMxNjHMimgKTkql9stpKUNO7HjBzIcGgorfAi\nIiIi+5pKyKTvrR0z3Gl/kvGTR/iJH7yPp85d5q25VawwwjSN2lSuZPpXEDZNIbPWn2tMCVtzrtvn\nVWuT0EzTwDKN2gpJBBiNvVzWStkmmbRJsZSMTd6KejkZJPvf9MLYaBERERlsCjDS16YuzfOFp1+n\n5AeslKq8fXWVqUvXeOjeo1SDaF2o6WYvk0Ihz9zc8o5eZzWIKPoBZT9orJhstOnkybE8k+NjPHDX\nCLZlkM3Y/IffeI75JZ+ozRACs9bIHzWdqH9pmkmECcKIwrCzo59LREREZKcpwEhfO3thmpIfsLjs\nN45VKiHnp2YoDGdxMjazC6XGOOXdLJ+K45hyJaTkB1SCqHHs0vR1zk3N8L3vL7QEEdsyeN+9BSZP\nHWfsyBC2aZBzbJyMjWkYnBg9CCyzuOyvG0JgmkZSFhdDNmNT8oPGSk+9XM62TM5MjO3a5xcRERHZ\nDgUY6WtziyVWSq27poS1DRvnFks4GbsxfevsheldCTBhFFEsB0mIqKWMShDywitXOX9xdt2mk4eG\n0kyeGuWR+wvknBRp22TISZFJWy3POzMx1tiYc36pnKy2xGDbJoZBYzzyk6dPtKxK1Ucnf/zRO9X/\nIiIiIvvepgHGdV0T+BzwIOADn/E879U1z8kBfwL8Hc/zXroVFyqyHYXhLG9fXW08jmrhBZLm9SCI\nWFj2GYFb3v/hV0OK5YBKNWxTJnaFkt+6YebJsYNMjo8mZWKmgZOxyWXs2mjk9Zr3tTEMg+VipWU0\nMiTjkdfuf1MYdjr2BYmIiIjsN92swPwI4HieN+m67mPAzwGfrJ90XfcR4JeA22/NJYps35mJMaYu\nXSOolWg194A0zxlbLlW5azS/4+8fxTFlP6BYDghqyy1bKRMzTYNcLbjUe1U20tzDc2N4wfqQ0k2v\nj4iIiMh+1E2AOQN8GcDzvGdrgaVZBvhR4De6ecORkRy2bW3+xF1UKOz8D66yPzxRyDO3XOGLf/4q\n1TCC0MAyk2BhWSZGrf8jjGKe/PC7u74XNnteNYhYLVUp+lVsJ81BJ02lGvKNizP8+fNv8s7casvz\nDx90+Oj7b+dDD97GgWyKlG1yIJsim7Eb17idz/7EB09s63tl5+nPGdkq3TOyVbpnZKt69Z7pJsAc\nBJaaHoeu69qe5wUAnuc9A+C6bldvuLBQ3PxJu+hWTJSS/eWJiTEK+TRnL0xz8dI1YiBtm1SCqNH/\ncUdhiDsOZ7u6FzrdM+2a8gEWlss8e3GW57x2ZWJ5Jk+N8sCJw9imgRmGGIGBgcXqcsiqbs2+oD9n\nZKt0z8hW6Z6Rrdrv98xG4aqbAHMdaH4Fsx5eRPardnu/fPaT442xyms9efrEtt8rjCJKfkixNtkL\nkjDz+vR1zndTJmYkk8Fyjo1lam9ZERERkY10E2CeAT4B/E6tB+bFW3tJIjdnbUhpNyZ5JxrYK9Uk\ntPiVG0359Wli56ZmGhPB6oYPpHnsPTemiSVjkFNkM9a2y8REREREBk03AeaLwMdc1z1H0vf8add1\nfxw44Hner9zSqxPZhrMXptsef+rc5ZZVmR/9yMktB5cojlktVbm6VCIIbyyrbFwmdmOamGUaZFIW\nOccmk9pfvWAiIiIivWDTAON5XgR8ds3hdaOSPc97fIeuSeSmzC0mKx/N+5wYQBjBuwpJb8pWN68M\nwtreLZWAwDAJwnjTMrGH7i3wWK1MzDAgm07KxGxLZWIiIiIi26WNLKXvFIazXJ5JdqSvq4ZJcCn7\nQcu+KJttXlmuJCOQm5vyK9WQv/jeLOe7KBOzTIOcY5PN2I0d70VERERk+xRgpO/U935pFsdgWQbL\npWpLgGm3eWUUxRT9gJIfEEbry8Sef3mOYrl1jsXJsTyT42ONMrG0bZJzbJy0/hMTERER2Un66Ur6\nzvjJI+RzKZaL1caYZMMwiOOYIIxanlsYdhpfV4OQYjmg3NSUH8cxr79znfMXN58mZgCZtMWQY5Pa\nZ3sdiYiIiPQLBRjpS3cdz7eUd5X8gMVlf13/yYfeO0rJT8rEqmFrmdh3Xk2miV1ZUyZ2+KDDB+8/\n1igT0xhkERERkd2jACN96czEWMso5WytbGwkn6FSjTh6KMMj9x/j+EiOpdVK43mbTRM7PT7K6Yfe\nxdJiqTYGOelv0RhkERERkd2hACN9qdN+L/fdPtyyd0sUb1wmlrJM3nfv0cY0MYBcJoV5ICKTVpmY\niIiIyG5TgJG+NX7yCOMnjxDHMSU/pOhXudY0maxeJtZxmtipUR5xj5FzbAzASVvknBRHh7PMVQNE\nREREZPcpwEjfCsKoMU2seVWlXib2zZeuUK60londfdtBJk+Ncn9tmphpQM5JkcvYmKbKxERERET2\nmgKM9JWpS/M8/Z13uLJQZPhA0udy7+3DjTKxc1MzvPRG+zKxyfFRRg/ngGTCWC6TIpux1N8iIiIi\nso8owEhfiKKYb71yhd//+uXGsfnrPl/6xhu89MYCr719fd00sbVlYgCZlEXOscmk1N8iIiIish8p\nwEhPqwZJmVjZD/j6C9ON40EYsVquUiwHzMwXW77n7tuSaWL33zmCaRoYBmTTyRjktWOWRURERGR/\nUYCRnhPHMeVKuG7vlmvXy/iVJLis7W1pVyZmmga5jK3+FhEREZEeogAjPSOMIorlpCk/auphqVRD\nvv3KVa4u+fjV1uBimQaFYYfP/PCpRplYyjLJOTZOWv0tIiIiIr1GAUb2Pb+arLasDSfXrpd59ruz\nPNdmmlg6ZTLkpHDSFh9/9M5GX8uQY5NWf4uIiIhIz1KAkX0pimPKfkCxHBA0LbfEccxr71zn/NQM\nL31/gaaFGFKWycnbDhLHESU/ZCSfTCF78J6j5DLqbxERERHpBwowsq80mvIrrXu31MvEzl+c6Wqa\nmGUa5BybbMbGVJmYiIiISN9QgJE9V2/KL/kBlSBqObdRmdjaaWIAabve36JbW0RERKQf6ac82TNh\nlJR6Ff2AaJtlYi+8epWUbfLeu48w5NikbPW3iIiIiPQzBRjZdfWm/Eo1bAkn3ZSJHc5n+Op33mkc\nX1jx+bPn32Ikn2H85JFd+gQiIiIislcUYGRXdGrKh62Vif32n76MAY0NKOtjkM9emFaAERERERkA\nCjByS3Vqyt+sTGztppMAmZTF9WIF214/TWxusXwLP4WIiIiI7BcKMLLjNmrK36xMbPLUKA83TRMz\nDMimbXJOMgb5+EiO2TXfB1AYdm7dBxIRERGRfUMBRnZMGEUUywElP2BNldiWp4l1GoN8ZmKMLzz9\neuNxyQ9YKVUpVwJ+6Q+mODMxplIyERERkT6mACM3za8kk8T8amsw2axM7KH7jvLYqdYysc3GINfD\nydkL03x/doWVUpV8NoWTtpldKDXCjUKMiIiISH9SgJFtiaKYUiVpyg/XLLdsVCY2ks/w2KnjPOIe\nI5uplYkBTtoit8kY5KlL85y9MM3cYonCcJbhoTRO+sbz66sxv/wHFzl18rBWY0RERET6kAKMbEk1\nSEYglyutI5ChViZ2cZbnvO7KxEwDck6KbMbCMtc35jebujTfUjo2u1Bien6VkQMZnIxNyQ9YXPaT\nkwZajRERERHpUwowsql6U36xHFANo3XnXnv7Oucvdl8mZlsGQ04KJ201xiBv5uyF6XXHDAyuLpWx\nLIMgiBrvbRoGZT/AydgarywiIiLSZxRgpKMgrI1AbtOU71dDvv3KHM9enO2qTAySMcg5xyaT6lwm\n1sncYut7lPyAMIyI4hgjpOX6DBMWln1G0HhlERERkX6jACPrdGrKB5i/XubZizM87811VSZmGJDN\n2OQyyRjk7SoMZ5ldKN2YOuYHANimSdS0wYxh0JhatlyqctdoftvvKSIiIiL7jwKMAElTftFPRiCv\nbcqvl4mdm5rBe6O7MjHLNBhybJw1Y5C368zEGL/5lZcbfS71zBITYxhgm8a66w7CiDMTYzf93iIi\nIiKyfyjADLhKtbba0qYpv14mdn5qdl0JV6cysbRtMuSkyKS3Xia2kfGTRxg5kGGlVCUII0zTaKy2\nhFGMVVvxiQEMsC2TOwpD6n8RERER6TMKMAMoimPKfkjRrxKEa2PLxmVi737XQU6fGsVtLhMDnFqZ\nWMrefpnYZipBSGE4C9Ayday+wGOaBiP5ZCoZwJOnT9yyaxERERGRvaEAM0CCMKJYDihVAuI1uWXD\nMjHb5KF7jzJ5apTjTWVipmmQqwWXepi5Fer7v1xZKBED+WyqseqzUqpiALcXhsAwqFQjCsOO9oAR\nERER6VMKMH0ujmP8ajICuRJE685vp0wsZZnkHHtLY5C3q3n/l6FsisVlvzFhLJuxyWZsPvXRuxVW\nRERERAaEAkyfCqOIkp/0t0RrZyCzvTKxTNpiyLFJ2Tvb37KR5v1fmlddVmoTxrTSIiIiIjJYFGD6\njF8NKXVoyo/jmFffXuL81Gz3ZWL1MciOjWXeuv6WTtauCtVXXUzD4LOfHN/16xERERGRvaUA0weS\npvyAYjkgaLPaslmZ2OSpUR52Cy1lYrZpkHNSZDO3vkxsI/X9X9Yfd/bgakRERERkrynA9LBqEFH0\nA8ptmvJh4zKxe951iMlTx1vKxAAyKYucY5NJ7V6Z2EbOTIw1emDWHhcRERGRwaMA02PiOKZcScrE\n2jXl3ygTm8F7Y7F9mdj4KMdHbpSJGQZk00mZmG3tfpnYZpyUyVtzqwDcfuwAT07epb4XERERkQGl\nANMjNmvK306ZmGkaDDk3ekr2m+YJZEdr+7+sXUkSERERkcGiALPP1Ucg+9X2P7hvWiY2Pop7x3BL\nmVh9DHJzmNmPmieQrT2uFRgRERGRwbS/f4IdUJs15W9WJvb++wo8dup4a5kYezMG+WasXUm6cby8\ny1ciIiIiIvuFAsw+sllTvl8N+fbLc5y/OLPuh/jD+QyPtSsT2+MxyN2aujTP2QvTzC2WKAxnOTMx\npglkIiIiIrKOAswe26wpH26UiT330ty6UrJOZWL7ZQxyN5p7XQBmF0p84enXedgttA0wmkAmIiIi\nMrgUYPbIZk35G5WJpW2Th9qUidXPDTkpMuneKBODzr0ub11Z4VMfvbu2MlOmMOxwZmJM/S8iIiIi\nA0wBZpdVqklo8SshbarENiwT6zRNzACcjM3QPh2DvJl2vS4lP+DipWuNkrIf/chJBRcRERERUYDZ\nDXEc11ZbqgRhu9gC80u1MjGv+zIx04CckyKXsVuO95q1vS4lP2Bx2ce2TaL4RkkZoBAjIiIiMuAU\nYG6hIEya8kt++6b8epnYuakZXt5CmZhtGQw5KZz0/u9v6caZibGWHpiVUhWAfDbV8jyNTxYRERER\nBZhboFxJRiB3asr3KyHfemWOZ7cwTQwgk7LIOTaZVO/0t3TLSZm8NbcKJNPYTMNgYcXHLlXJZ1M4\nGVvjk0VEREREAWanRFHcWG0J2zTlw/bKxHq9v2UzzRPIhrIpllZ8qkGEYYBlGgRxxMKyzwhw12h+\nby9WRERERPacAsxNqgYhxXJAuUNTfhTHvLZJmdjkqVGOjWRbvs80DXIZu+f7WzYydWmez3/pJVZK\nVQwgjOLGRLY4hjCMwQLTMFguVTU+WUREREQUYLajm6b8epnY+akZri6tLxObHB/l/fetLxPrt/6W\nTuorLyulKsRQDSPiGGKSVacYMIxkZSudscjn0up/EREREREFmK3YrCkfkjKx8xdneL5Dmdjp8VHu\nW1MmBv3d39JOfe8X2zIJgmjd76dpJOcwkillx9esUImIiIjIYFKA6cJmTflRHPPqW8mmky+/2X2Z\nWL/3t2ykvvfLgWyKxWUfw6AlxNQDXv33ReVjIiIiIgIKMB3Vm/KLftDoy1irmzKxh90CTrr1t3kQ\n+ls2U9/7pV5Ct7TiUwkiLMMgn0tRCSKCMOKOYwd4cvIulY+JiIiICKAAs85mTfmwcZnYvbcfYvJU\n+zKxQelv6Ubz3i/ZjE02Y1PyA0byGSrViMKww5mJMQUXEREREWmhAEN3TfnbLRODwetv6UY9mJy9\nMM3cYlmBRURERES6MtABJggjllZ85hZLdKgS27hM7GCGyVPty8QMA7Jpm9wA9rd0a/zkEQUWlP/p\nsQAACmZJREFUEREREdmSgQwwfiWk6Af41ZDDltU2vGxaJlafJramFMwyDXJOUhK19pyIiIiIiNyc\ngQkw9ab8kh8QdlhuaS4T895cbDmXtk3ef1+Bx8ZHOTa8vkwsbZvkHHvdSoy0N3VpvlY+VqIwnFX5\nmIiIiIh0pe9/2q5UQ0r+xk35fiXkWy/Pcf7iFsvEACdtkXNSpGyViXWrvoll3exCqfFYIUZERERE\nNtKXASaKY8qbNOUDXF0q8SfPv8W5C9NbKhMzDcg5KbIZC8tUcNmq+iaW7Y4rwIiIiIjIRvoqwARh\nRLEcUKoE63Z2r6uXiZ2rTRNrlk6ZvP/ezmViGoO8M+qbWK4/Xm57XERERESkri8CTLkSUCwHVIJo\nw+d86+WrPLvFMjHQGOSdVt/Ecv1xZw+uRkRERER6Sc8GmHpTftEPiDrNQCYpEzt/cZZvdZgm9oOP\nnWBsxFlXJmYYyQaLuYzGIO+05k0s1x4XEREREdlIzwWYSrU2AnmDpvytlIkdPjzEtWurjfOWaTDk\n2Dgag3zLaBNLEREREdmunggw3Tbl30yZmMYg7y5tYikiIiIi27Gvf1rvpikfNi8TOz0+yr1tpokZ\nQM6xMQ46GoMsIiIiItID9mWA6aYpf9MysfsKTJ4apdBmmphpGuRq/S0jeYegXN3xzyAiIiIiIjtv\n3wSYbpvy62Vi5y/OML+mTOzIQYfHTh3vWCaWsuplYhqDLCIiIiLSi/Y8wHTTlA/bLxMDcNIWuYxN\nWmOQRURERER62p4EmDiOKXXRlB/FMa+8ucj5i7NbLhOrj0EecmwsU/0tIiIiIiL9YNcDzEqpymq5\numFT/kZlYptNE7NNIykT0xjkfWnq0nxtfHKJwnBW45NFREREZEt2PcCUN5godjNlYmnbZMhJkUmr\nTGy/+qPzl/nyN94gCCNsy6RYDphdKAEoxIiIiIhIV/a8B+amysQAp1YmZlsqE9vPpi7NJ+GlNlku\nCCIWln1GSDa0VIARERERkW7sWYDZbJrY5Phx3n9f+zKx5jHIprm9MrGpS/M8de4yb18tUg1CTMMg\nAohjTMMgiGKiKMY0DWxzZ871+uvfzHtXqyH14XKmkfw7NA2D5VKVucXWf/8iIiIiIp1sGmBc1zWB\nzwEPAj7wGc/zXm06/wng3wIB8Gue5/3qRq83t1ji7IUZnn/5CpVq6z4v991xiMlTncvEdmoM8tSl\neX7zKy+zuOwTx1ANO+83E0YxnXaJ2e65Xn/9m33vKIY4jMFKNistDDtdvJqIiIiISHcrMD8COJ7n\nTbqu+xjwc8AnAVzXTQH/BfgAsAo847ru//U8b7bTi/3n3/5Oy+NuysQyaYshxyZl70x/y9kL06yU\nkh+tww32nJFbK4pi0imLMxNje30pIiIiItIjugkwZ4AvA3ie96zruo80nXsAeNXzvAUA13XPAh8B\nfnezFy2MZHni4TuYfO8Y2cz6yzAMGHJSHMimsHa4v2VhpUIYxhiGQUzn1Re5hQzAMPjUX7qXJz54\nYq+vZssKhfxeX4L0GN0zslW6Z2SrdM/IVvXqPdNNgDkILDU9Dl3XtT3PC9qcWwYObfRiH7i/wHtO\nHG6UiZVWfUqr/o0LahqDXClVuFaqdP9pujRyIM3blkEQRBgYxBtuoSk7zTSS4Qt3FIZ4YmKMubnl\nvb6kLSkU8j13zbK3dM/IVumeka3SPSNbtd/vmY3CVTcB5jrQ/ApmLby0O5cHWkeJrfHXnrin7eaV\nuzkG+czEGJdnlllc9rFMg2iDzTRl5xiAZRkcOejgZGyePH1iry9JRERERHpMNwHmGeATwO/UemBe\nbDr3PeBe13UPAysk5WM/2+2b18cg5zI2KXv3xiCPnzzCT/zgfTx17jLvzBcxq8lUrDiGOE6mZwVh\n0yQta2fO9frr3+x7p1MWQ06KO48f0AaWIiIiIrIt3QSYLwIfc133HEnm+LTruj8OHPA871dc1/2n\nwB8DJskUsrc3e8GdGIN8s8ZPHmH85JF9v3wmIiIiIiI3bBpgPM+LgM+uOfxS0/k/BP6w2zfM59Kk\nbfOmxiCLiIiIiMhg2vWNLDOpW9/jIiIiIiIi/Wn3Gk9ERERERERukgKMiIiIiIj0DAUYERERERHp\nGQowIiIiIiLSMxRgRERERESkZyjAiIiIiIhIz1CAERERERGRnqEAIyIiIiIiPUMBRkREREREeoYC\njIiIiIiI9AwFGBERERER6RkKMCIiIiIi0jMUYEREREREpGcowIiIiIiISM9QgBERERERkZ6hACMi\nIiIiIj3DiON4r69BRERERESkK1qBERERERGRnqEAIyIiIiIiPUMBRkREREREeoYCjIiIiIiI9AwF\nGBERERER6RkKMCIiIiIi0jMUYEREREREpGfYe30Be8F1XRP4HPAg4AOf8Tzv1b29KtlvXNd9FPhP\nnuc97rruPcDngRiYAv6h53mR67p/F/j7QAD8jOd5f7RnFyx7xnXdFPBrwAkgA/wM8F10z0gHruta\nwK8CLsk98lmgjO4Z2YTruseA54GPkdwTn0f3jHTguu63gOu1h5eAf08f3DODugLzI4Djed4k8C+B\nn9vj65F9xnXdfw78N8CpHfp54Kc8z/swYACfdF13FPhHwIeAHwL+o+u6mb24XtlzPwHM1+6PjwP/\nFd0zsrFPAHie9yHgp0h+qNA9Ixuq/WXJLwOl2iHdM9KR67oOYHie93jt16fpk3tmUAPMGeDLAJ7n\nPQs8sreXI/vQa8CPNT1+GHi69vWXgB8APgg843me73neEvAqMLGrVyn7xe8CP1372iD5GyzdM9KR\n53m/D/y92sO7gEV0z8jmfhb4JeCd2mPdM7KRB4Gc67pfcV33/7mu+xh9cs8MaoA5CCw1PQ5d1x3I\ncjppz/O8LwDVpkOG53lx7etl4BDr76P6cRkwnueteJ637LpuHvg9kr9R1z0jG/I8L3Bd99eBXwT+\nF7pnZAOu6/5tYM7zvD9uOqx7RjZSJAm9P0RSpto3f84MaoC5DuSbHpue5wV7dTHSE6Kmr/Mkf1u6\n9j6qH5cB5LruHcCfA7/hed5voXtGuuB53t8C7iPph8k2ndI9I2v9JPAx13W/CrwP+J/Asabzumdk\nrZeB3/Q8L/Y872VgHjjedL5n75lBDTDPAH8FoLac9uLeXo70gG+7rvt47eu/DHwd+Avgw67rOq7r\nHgIeIGmIkwHjuu5x4CvAv/A879dqh3XPSEeu6/4N13X/Ve1hkSTwPqd7RjrxPO8jnud91PO8x4Hv\nAH8T+JLuGdnAT1Lr83Zd9zaSlZav9MM9M6hlU18k+VuMcyT16p/e4+uR/e+fAb/qum4a+B7we57n\nha7r/gLJf/wm8G88zyvv5UXKnvnXwAjw067r1nth/jHwC7pnpIP/A/wP13W/BqSAf0Jyn+jPGdkK\n/b9JNvLfgc+7rnuWZOrYTwJX6YN7xojjePNniYiIiIiI7AODWkImIiIiIiI9SAFGRERERER6hgKM\niIiIiIj0DAUYERERERHpGQowIiIiIiLSMxRgRERERESkZyjAiIiIiIhIz/j/34sDtNbk9M4AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ba0c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kcDjGXWVq72EdE7ITgQkAABtZlqUfPWqqtSvxYTTH49Td71in3JG/7AN2euO0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\nYTWy08gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136bb0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "alpha阿尔法:0.1310\n",
      "beta贝塔:0.1100\n",
      "Information信息比率:0.0210\n",
      "策略Sharpe夏普比率: 1.8907\n",
      "基准Sharpe夏普比率: 0.5012\n",
      "策略波动率Volatility: 0.0742\n",
      "基准波动率Volatility: 0.1689\n"
     ]
    },
    {
     "data": {
      "image/png": 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c6xHoGhpa2OsRAJhEBgcH7Bsgqw/8cZ1jsyqllClJTkuyQ5I31lpHSim3J5lXax1Jcnsp\n5aEkWya5Z02+CwAAYFXW9KpoX0gyPckBo5akHZbk9CQppWyVZFaSX63h9wAAAKzSMz5iU0o5OJ1l\nZz9M8s4k1ya5ppSSJGckOT/JhaWU65KMJDlsrGVoAAAAa2JcYVNrvSvJbt0/f3nUplUd8Tl4zcYC\nAAAYPzfoBAAAmidsAACA5gkbAACgecIGAABonrABAACaJ2wAAIDmCRsAAKB5wgYAAGiesAEAAJon\nbAAAgOYJGwAAoHnCBgAAaJ6wAQAAmidsAACA5gkbAACgecIGAABonrABAACaJ2wAAIDmCRsAAKB5\nwgYAAGiesAEAAJonbAAAgOYJGwAAoHnCBgAAaJ6wAQAAmidsAACA5gkbAACgecIGAABonrABAACa\nJ2wAAIDmCRsAAKB5wgYAAGiesAEAAJonbAAAgOYJGwAAoHnCBgAAaJ6wAQAAmidsAACA5gkbAACg\necIGAABoXv94XlRK2TXJqbXWvVZ4fr8kxydZmuSCWuu5pZQpSc5MMifJcJLDa63zJnRqAACAUcY8\nYlNKOTrJeUmmr/D8tCSfSfLaJK9KcmQp5beSHJBkeq119yTHJDl9oocGAAAYbTxHbH6W5KAkF63w\n/I5J5tVa5ydJKeW6JK9MsnuSbyVJrfXGUsou4xlk9uwZ6e+fOt65WQtmztyo1yPQNTg40OsRAJhk\n7Btg9cYMm1rrZaWUbVeyaVaSR0Y9Xphk05U8/2Qppb/WunR13zN//uKxp2WtWrRouNcj0DU0tLDX\nIwAwiQwODtg3QFYf+Gty8YAFSUZ/8kCSh1fy/JSxogYAAGBNjOviAavwkyQvKqVsluTRdJahfTrJ\nSJL9kny1lLJbkpvXeEoAAIDVeMZHbEopB5dSjqy1PpHkr5L8a5Ib0rkq2i+SXJ7k8VLK99K5uMBR\nEzkwAADAivpGRkZ6PUOSZGho4eQYZAN2xbV39HoEkhxx0BzrqAF4GufYQMfg4EDfqra5QScAANA8\nYQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANE/YAAAAzRM2AABA84QNAADQPGEDAAA0T9gAAADN\nEzYAAEDzhA0AANA8YQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANE/YAAAAzRM2AABA84QNAADQ\nPGEDAAA0T9gAAADNEzYAAEDzhA0AANA8YQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANE/YAAAA\nzRM2AABA84QNAADQPGEDAAA0T9gAAADNEzYAAEDzhA0AANA8YQMAADRP2AAAAM3rH+sFpZQpSc5M\nMifJcJLDa63zutt+O8klo17+0iTH1FrPLqXMTbKg+/ydtdZDJ3RyAACArjHDJskBSabXWncvpeyW\n5PQk+ydJrfW+JHslSSll9yQnJzm3lDI9SV+tda+1MTQAAMBo41mKtmeSbyVJrfXGJLus+IJSSl+S\nzyd5d631yXSO7swopVxVSrmmG0QAAABrxXiO2MxK8siox0+WUvprrUtHPbdfkltrrbX7eHGSTyc5\nL8mLknyzlFJWeM/TzJ49I/39U5/Z9EyomTM36vUIdA0ODvR6BAAmGfsGWL3xhM2CJKP/SZqykkA5\nJMkZox7fnmRerXUkye2llIeSbJnknlV9yfz5i8c3MWvNokXDvR6BrqGhhb0eAYBJZHBwwL4BsvrA\nH89StOuT7JMk3SVlN6/kNbsk+d6ox4elcy5OSilbpXPU51fjGxcAAOCZGc8Rm8uTvKaU8r0kfUkO\nLaUcnGSTWus5pZTBJAu6R2eWOz/JhaWU65KMJDlsdcvQAAAA1sSYYVNrXZbkXSs8fduo7UPpXOZ5\n9HuWJDl4IgYEAAAYixt0AgAAzRM2AABA84QNAADQPGEDAAA0T9gAAADNEzYAAEDzhA0AANA8YQMA\nADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANE/YAAAAzRM2AABA84QNAADQPGEDAAA0T9gAAADNEzYA\nAEDzhA0AANA8YQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANE/YAAAAzRM2AABA84QNAADQPGED\nAAA0T9gAAADNEzYAAEDzhA0AANA8YQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANE/YAAAAzRM2\nAABA84QNAADQPGEDAAA0r3+sF5RSpiQ5M8mcJMNJDq+1zhu1/agkhycZ6j7150l+urr3AAAATKTx\nHLE5IMn0WuvuSY5JcvoK21+e5E9rrXt1/1PH8R4AAIAJM56w2TPJt5Kk1npjkl1W2P7yJMeWUq4r\npRw7zvcAAABMmDGXoiWZleSRUY+fLKX011qXdh9fkuTvkixIcnkpZd9xvOc3zJ49I/39U5/Z9Eyo\nmTM36vUIJPnqLV/v9QgkectO+/Z6BICnGRwc6PUIMKmNJ2wWJBn9T9KU5YFSSulL8tla6yPdx99I\n8rLVvWdV5s9f/EzmZi1YtGi41yOQZNMki/1d9NzQ0MJejwDwlMHBAf9egqw+8MezFO36JPskSSll\ntyQ3j9o2K8ktpZRNupGzd5IfjfEeAACACTWeIzaXJ3lNKeV7SfqSHFpKOTjJJrXWc0opxyX5TjpX\nP7u61npl90pqT3vPWpofAAAgfSMjI72eIUkyNLRwcgyyAbvi2jt6PQJJNt3hHkvRJoHXb/faXo8A\n8BRL0aBjcHCgb1Xb3KATAABonrABAACaJ2wAAIDmCRsAAKB5wgYAAGiesAEAAJonbAAAgOYJGwAA\noHnCBgAAaJ6wAQAAmidsAACA5gkbAACgecIGAABonrABAACaJ2wAAIDmCRsAAKB5wgYAAGiesAEA\nAJonbAAAgOYJGwAAoHnCBgAAaJ6wAQAAmidsAACA5gkbAACgecIGAABonrABAACaJ2wAAIDmCRsA\nAKB5wgYAAGiesAEAAJonbAAAgOYJGwAAoHnCBgAAaJ6wAQAAmidsAACA5gkbAACgecIGAABonrAB\nAACaJ2wAAIDmCRsAAKB5/WO9oJQyJcmZSeYkGU5yeK113qjtb03ygSRLk9yc5C9qrctKKXOTLOi+\n7M5a66ETPTwAAEAyjrBJckCS6bXW3UspuyU5Pcn+SVJK2TjJSUleUmtdXEr5SpJ9SylXJemrte61\nluYGAAB4yniWou2Z5FtJUmu9Mckuo7YNJ9mj1rq4+7g/yePpHN2ZUUq5qpRyTTeIAAAA1orxHLGZ\nleSRUY+fLKX011qX1lqXJbk/SUop70uySZJvJ9kpyaeTnJfkRUm+WUoptdalq/qS2bNnpL9/6rP8\nMZgIM2du1OsR6Jrh76LnBgcHej0CwNP49xKs3njCZkGS0f8kTRkdKN1zcE5LskOSN9ZaR0optyeZ\nV2sdSXJ7KeWhJFsmuWdVXzJ//uJVbWIdWbRouNcjkGTTJIv9XfTc0NDCXo8A8JTBwQH/XoKsPvDH\nsxTt+iT7JEl3SdnNK2z/QpLpSQ4YtSTtsHTOxUkpZat0jvr86hlNDQAAME7jOWJzeZLXlFK+l6Qv\nyaGllIPTWXb2wyTvTHJtkmtKKUlyRpLzk1xYSrkuyUiSw1a3DA0AAGBNjBk23fNo3rXC07eN+vOq\njvoc/GyHAgAAeCbcoBMAAGiesAEAAJonbAAAgOYJGwAAoHnCBgAAaJ6wAQAAmidsAACA5gkbAACg\necIGAABonrABAACaJ2wAAIDm9fd6AAAAVu+rt3w9ixcN93qMDdrrt3ttr0dgDI7YAAAAzRM2AABA\n84QNAADQPGEDAAA0T9gAAADNEzYAAEDzhA0AANA8YQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAA\nNE/YAAAAzRM2AABA84QNAADQPGEDAAA0T9gAAADN6+/1AADA5HXFtXf0egSSbLpDryeAyc8RGwAA\noHnCBgAAaJ6wAQAAmidsAACA5gkbAACgecIGAABonrABAACaJ2wAAIDmCRsAAKB5wgYAAGhe/1gv\nKKVMSXJmkjlJhpMcXmudN2r7fkmOT7I0yQW11nPHeg8AAMBEGs8RmwOSTK+17p7kmCSnL99QSpmW\n5DNJXpvkVUmOLKX81ureAwAAMNHGEzZ7JvlWktRab0yyy6htOyaZV2udX2tdkuS6JK8c4z0AAAAT\nasylaElmJXlk1OMnSyn9tdalK9m2MMmmY7xnpQYHB/rGPzZrwxEHzen1CCTprOAEmBzsGyYLfw8w\nlvEcsVmQZGD0e0YFyorbBpI8PMZ7AAAAJtR4wub6JPskSSlltyQ3j9r2kyQvKqVsVkp5TjrL0G4Y\n4z0AAAATqm9kZGS1Lxh1hbOdk/QlOTTJ7yfZpNZ6zqirok1J56pof7ey99Rab1t7PwYAALAhGzNs\nAAAAJjs36AQAAJonbAAAgOYJGwAAoHnCBgAAaJ6wgYaUUvpKKW5mC8AqLd9P2F+woRE20IhSypRa\n60itdaSU0t/reQCYnLr7ialJXpgIHDYcwgYaUWtdliSllJOSfKaU8q4ejwTAJLCKcDk4yVFJJ3TW\n7UTQG37rC5PU8h1V9zdvfUlmJPlkkqEkJyT5USllYZIv22kBbHi6N0T//STbJPmnUsqOSfpqrT9O\nsizJ/+u+bmqt9cneTQrrhht0wiTUXXa2/AjNdknuTDI1yWeTXJ/kZUl2TvL9WusJPRsUgHWulPKc\nJPsm+XGSnyf5RZIzkmybZDjJvye5Jclnaq3/s0djwjpnKRpMIt3fvqXWuqyUMqOU8tEk/5Lk0+ks\nKRhK8rEk36q1/nGSTUsp2/dsYADWqVLK25JsnU7U3J7kBUluS/LHtdZ3pBM4x6bzy697Symb92hU\nWOeEDUwio47S7Jvk4iRLk+ya5Mokr0qyOMk/J3lVKeUHSR5KckdvpgVgXSmlvL6UsmmSvZMcmuS+\ndH7htXeSP04yu5Syd3cZ2vuTvDjJ65I80qORYZ0TNtBDK57wWUp5XSnli0mel2TzJPfUWh9Ncl06\na6VvSfKpJHcneXOt9ePOrwFYf5VSZnT/OJLk8SQnJtk9nWVnS9I5v+bJdI7mn1ZKGUzy77XWY5PM\nTSd8YIMgbKBHuidzjox6vEWS05PcW2u9IMn5SV7bfX7bJK9Icnut9f5a6wW11rtLKVNcxhNg/VRK\nmZ7kf5RSdkjyr0nel2T7JF9McniSf0vn/8v9Sa31H5Lcm85R/e277304yV09GB16QthAj9Ran+ye\nR3NqKeU9STZO8rkke3S3fymdKxdemOSUJJfWWn82+sZrtdZljtgArF9GnW/5eJI5Sb6c5J3pLD/7\nfHf/8DtJnp/kB0l2LaXMSfL2JH9Ua709naul/TjJvHX/E0BvCBtYR7o3Sxv9+JVJvp3kiXR2Tu9O\n50IBvyylvLP7ss8mGUjy4Vrrecl/349A0ACsX0opfaOvitn143SWJvfVWi9OZx9xZJKTk/x1OkuU\nr0lyZ6114ajLOt9Yaz1phc+C9ZrLPcNaVkp5cZIptdabu49n1VoXlFLenM6ygTvSuT/NrCRfSCd0\n/jbJ79dah0spf5/kgST/u9b6RE9+CADWmlLKc2utD496vEM64TI3yWVJdkjy6nTuYbZ5kpvS+YXY\n/05yZq31l+t8aJiEhA2sRaWUTZK8J50lAyckOTWdgPlcOssD9k/y0iRnp3MVm02T/FWS6bXWW7uf\n8fwk29Zar1/nPwAAa1Up5YVJLkpyRK21llIOSfKn6ZxH88J0oubQJOcl+a90jvS/J8kltdbrRn1O\nnyP5bOiEDawFo+/y3D1ic1SSndJZWvZEkjNqrS8opXw+ydVJZiQ5OMn5tdbLR32OHRXAeqh7Hs1I\nrXWklPK5JAuSfCTJPumc9D81yf9K5xyb45Lck85RnP9KcuryfYP9BPw3YQNrUSnl4CR7pnNVmkNq\nrTt3n/9Gkv+TztrpE7rbP1Rrfag3kwKwLnQvANM3+tyXUsrzklya5Jgk89OJm63SWZZ8bJK90vnl\nWH+tdXj55wgaeDphA2tB974D5yV5LMnlSZ6Tzo3S/m+t9exSSklya5LNkjxZa13Ufd+KJ40CsJ4Y\nHSPdJWgfS3J/OlHziiQvTydkjk3yoyR/kGRakitqrd9e/hmJC8jAyvT3egBYT03r/vc/J9k1yZZJ\ntkuyTSn00/kEAAABg0lEQVTl6u466t1qrQuWv0HUAKzfusvOpiR5W5I3Jvl6kqF0brx8TJLXp3Pe\n5dwkr0ry81rrySt+xjodGhriiA2sBd1LO/9hOvekuSfJfyY5KcmdSb65/DdvAGxYSil/ks65M/fW\nWt/Xfe5v07lK5s3pXEjmLUkWjjpX0y++YBzcxwbWgu7O6IYkP03n6M1JSW5PcpyoAdiwlFJ+r7v0\nLOncm6wmua2UslP3ue8n2bjW+o0kR9ZaH+7exLlv+c2YezE3tMYRG1iLuneCPjKdy3Je233OCZ8A\nG5BSynuSvDjJd9JZZnZlOudfzk7n6mcvS/KeWuuN3dfbT8CzIGxgHbKcAGDD0z3hf4ckX0tyWpJd\nkpzefe6FtdYLejgerDeEDawDggaAUsor07lowJ8n2aTWunTUtqfufwY8O8IGAGAdKqXsUmv94fIl\nZ5aewcQQNgAA65iYgYknbAAAgOa53DMAANA8YQMAADRP2AAAAM0TNgAAQPOEDQAA0DxhAwAANO//\nA0xFTC44AnvBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114905990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from abupy import AbuMetricsBase\n",
    "best_score_tuple_grid = grid_search.best_score_tuple_grid\n",
    "AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd, best_score_tuple_grid.action_pd,\n",
    "                                        best_score_tuple_grid.capital, best_score_tuple_grid.benchmark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 度量结果的评分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读者可能会有的疑问1：哪里来的分数，怎么评定的分数。\n",
    "\n",
    "在GridSearch类 fit()函数中可以看到第二个参数score_class需要一个判分类，在fit()中最后几行使用ABuMetricsScore.make_scorer()函数将所有返回的结果score_tuple_array使用判分类WrsmScorer对结果打分数。\n",
    "\n",
    "打分数实现的基本思路为：如果想根据sharpe值的结果来对最优参数下判断，但是sharpe值多大可以判定为100分？多大可以判断为50分呢？我们无法确定，所以将所有sharpe结果排序，由于sharpe值越大越好，所以排序结果对应分数由0至1，这样就可以得到某一个具体参数组合的sharpe值的分数。这样的话使用多个评分标准， 比如这里使用'win_rate'、 'returns'、 'sharpe'、'max_drawdown'四种，就可以分别计算出某个参数组合对应的度量指标评分，再乘以分配给它们的权重就可以得到最终结果分数。\n",
    "\n",
    "\n",
    "具体请阅读GridSearch.fit，AbuBaseScorer，WrsmScorer源代码实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下代码我们实例化一个评分类WrsmScorer，它的参数为之前GridSearch返回的score_tuple_array对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import WrsmScorer\n",
    "scorer = WrsmScorer(score_tuple_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如下可以看到scorer中的score_pd是由评分的度量指标数值，以及这个具体数据对应项所得的分数组成："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win_rate</th>\n",
       "      <th>returns</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>score_win_rate</th>\n",
       "      <th>score_returns</th>\n",
       "      <th>score_sharpe</th>\n",
       "      <th>score_max_drawdown</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>0.6053</td>\n",
       "      <td>0.2989</td>\n",
       "      <td>1.6663</td>\n",
       "      <td>-0.0479</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.9308</td>\n",
       "      <td>0.9860</td>\n",
       "      <td>0.9776</td>\n",
       "      <td>0.9608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>0.6053</td>\n",
       "      <td>0.2966</td>\n",
       "      <td>1.6902</td>\n",
       "      <td>-0.0407</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.9301</td>\n",
       "      <td>0.9895</td>\n",
       "      <td>0.9972</td>\n",
       "      <td>0.9664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0.6053</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>1.8297</td>\n",
       "      <td>-0.0462</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.9406</td>\n",
       "      <td>0.9979</td>\n",
       "      <td>0.9839</td>\n",
       "      <td>0.9678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>0.6053</td>\n",
       "      <td>0.3107</td>\n",
       "      <td>1.7337</td>\n",
       "      <td>-0.0424</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.9413</td>\n",
       "      <td>0.9923</td>\n",
       "      <td>0.9958</td>\n",
       "      <td>0.9696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>0.6053</td>\n",
       "      <td>0.3158</td>\n",
       "      <td>1.8907</td>\n",
       "      <td>-0.0375</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.9469</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     win_rate  returns  sharpe  max_drawdown  score_win_rate  score_returns  \\\n",
       "677    0.6053   0.2989  1.6663       -0.0479           0.949         0.9308   \n",
       "695    0.6053   0.2966  1.6902       -0.0407           0.949         0.9301   \n",
       "815    0.6053   0.3090  1.8297       -0.0462           0.949         0.9406   \n",
       "671    0.6053   0.3107  1.7337       -0.0424           0.949         0.9413   \n",
       "665    0.6053   0.3158  1.8907       -0.0375           0.949         0.9469   \n",
       "\n",
       "     score_sharpe  score_max_drawdown   score  \n",
       "677        0.9860              0.9776  0.9608  \n",
       "695        0.9895              0.9972  0.9664  \n",
       "815        0.9979              0.9839  0.9678  \n",
       "671        0.9923              0.9958  0.9696  \n",
       "665        1.0000              1.0000  0.9740  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorer.fit_score()\n",
    "scorer.score_pd.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于AbuBaseScorer 中fit_score()函数的实现只是对score_pd的'score'项进行排序后返回score，这样最终的结果为分数及对应score_tuple_array的序列号，从上面输出可以看出665为最优参数序号。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 不同权重的评分\n",
    "\n",
    "读者可能会有的疑问2：为什么从度量可视化看到的这个最优的投资回报还没有上一节的那个回测高？\n",
    "\n",
    "因为由于默认的WrsmScorer使用：胜率、sharpe、投资回报、最大回撤这四个因素，综合评定策略的分数，并且类GridSearch默认为四项等权重评分，我们下面可以仍然使用WrsmScorer但是通过调整权重来达到各种评定效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:51\n",
      "买入后尚未卖出的交易数量:19\n",
      "胜率:82.3529%\n",
      "平均获利期望:16.2075%\n",
      "平均亏损期望:-7.6414%\n",
      "盈亏比:8.8162\n",
      "策略收益: 49.7671%\n",
      "基准收益: 15.0841%\n",
      "策略年化收益: 24.8835%\n",
      "基准年化收益: 7.5420%\n",
      "策略买入成交比例:62.8571%\n",
      "策略资金利用率比例:41.3748%\n",
      "策略共执行504个交易日\n",
      "alpha阿尔法:0.1810\n",
      "beta贝塔:0.3710\n",
      "Information信息比率:0.0485\n",
      "策略Sharpe夏普比率: 1.4955\n",
      "基准Sharpe夏普比率: 0.5012\n",
      "策略波动率Volatility: 0.1420\n",
      "基准波动率Volatility: 0.1689\n"
     ]
    }
   ],
   "source": [
    "# 实例化WrsmScorer，参数weights，只有第二项为1，其他都是0，\n",
    "# 代表只考虑投资回报来评分\n",
    "scorer = WrsmScorer(score_tuple_array, weights=[0, 1, 0, 0])\n",
    "# 返回排序后的队列\n",
    "scorer_returns_max = scorer.fit_score()\n",
    "# 因为是倒序排序，所以index最后一个为最优参数\n",
    "best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]\n",
    "# 由于篇幅，最优结果只打印文字信息\n",
    "AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,\n",
    "                            best_score_tuple_grid.action_pd,\n",
    "                            best_score_tuple_grid.capital,\n",
    "                            best_score_tuple_grid.benchmark,\n",
    "                            only_info=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到只考虑投资回报来评分的话，上面策略收益: 50.44%为最高。\n",
    "\n",
    "下面通过best_score_tuple_grid.buy_factors，best_score_tuple_grid.sell_factors看一下这个收益的买入因子参数、卖出因子参数，如下所示，可以发现它的因子参数组合与我在上一节开始使用的参数是一摸一样的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([{'class': abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak, 'xd': 42},\n",
       "  {'class': abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak, 'xd': 60}],\n",
       " [{'class': abupy.FactorSellBu.ABuFactorCloseAtrNStop.AbuFactorCloseAtrNStop,\n",
       "   'close_atr_n': 1.5}])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面看一下只考虑胜率来评分的结果，从结果可以看到虽然胜率达到了85%以上，但是收益并不高："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:21\n",
      "买入后尚未卖出的交易数量:9\n",
      "胜率:85.7143%\n",
      "平均获利期望:19.0903%\n",
      "平均亏损期望:-9.2893%\n",
      "盈亏比:9.4105\n",
      "策略收益: 21.5035%\n",
      "基准收益: 15.0841%\n",
      "策略年化收益: 10.7518%\n",
      "基准年化收益: 7.5420%\n",
      "策略买入成交比例:86.6667%\n",
      "策略资金利用率比例:44.3042%\n",
      "策略共执行504个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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1yuvxOu8cczYXzTqfcDTMTzf9mj3t+3i2ejU72nbzwv5XhnxOITRRNYrL10ah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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ae8190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3N7AQjWEtka24T5aA2XPjmIuEcenMGBS5/UFEvbqWQcEAhogGXiuCkKMs9Jv5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\nV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Qe4xRizC5ghWT72wXUf5To7PD7G2157iOMnn+AHU7P4zaQrlnPgXNI9qxVlOmkF63Nt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\nEREZGP8PHD+V1obaAVkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11447b3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zSfPqZ1gu5na72LyqLDZd29BNQ1vvGFuIiFP0DS4iIjLFjpxuo7GtDwCXC25Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ca2310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "alpha阿尔法:0.0724\n",
      "beta贝塔:0.4226\n",
      "Information信息比率:0.0092\n",
      "策略Sharpe夏普比率: 0.7392\n",
      "基准Sharpe夏普比率: 0.5012\n",
      "策略波动率Volatility: 0.1464\n",
      "基准波动率Volatility: 0.1689\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([{'class': abupy.FactorBuyBu.ABuFactorBuyBreak.AbuFactorBuyBreak, 'xd': 60}],\n",
       " [{'class': abupy.FactorSellBu.ABuFactorCloseAtrNStop.AbuFactorCloseAtrNStop,\n",
       "   'close_atr_n': 1.5}])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evrln+eeTfH6a5wIAAPgVbmQJAAA0Q8AAAADNEDAAAEAzBAwAANAMAQMAADRDwAAAAM0Q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\njiFvtv/iqCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c8e6410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 只有第一项为1，其他都是0代表只考虑胜率来评分\n",
    "scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])\n",
    "# 返回按照评分排序后的队列\n",
    "scorer_returns_max = scorer.fit_score()\n",
    "# index[-1]为最优参数序号\n",
    "best_score_tuple_grid = score_tuple_array[scorer_returns_max.index[-1]]\n",
    "AbuMetricsBase.show_general(best_score_tuple_grid.orders_pd,\n",
    "                            best_score_tuple_grid.action_pd,\n",
    "                            best_score_tuple_grid.capital,\n",
    "                            best_score_tuple_grid.benchmark,\n",
    "                            only_info=False)\n",
    "\n",
    "# 最后打印出只考虑胜率下最优结果使用的买入策略和卖出策略\n",
    "best_score_tuple_grid.buy_factors, best_score_tuple_grid.sell_factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从输出可以看到买入策略只使用60天突破买入作为买入信号，卖出只以保护利润的止盈ABuFactorCloseAtrNStop发出卖出信号，其实这种策略就是没有止损的策略。很像大多数普通交易者的交易模式，亏损了的交易不卖出，持有直到可以再次盈利为。这样的方式投资者的胜率非常高，我之前看过几个朋友的交易账户，发现他们交易的胜率非常高，但他们的账户最终都是亏损的，我认为交易中最虚幻的就是胜率，但是大多数人追求的反而是胜率。如果说股票市场最终的投资结果90%的人将亏损收场话，那我相信这90%的人胜率大多数都为超过50%甚至更高，那么如果我们想最终战胜市场的话，我们的有效投资策略是不是应该是胜率低于50%的呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 自定义评分类的实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的评分全部都是使用abupy中内置的WrsmScorer做为评分类，其从四个维度：胜率、sharpe、投资回报、最大回撤综合评定策略的分数，通过调整权重来达到各种评定效果，但是如果用户想要从其它的维度来综合评分的话就需要自定义评分类。\n",
    "\n",
    "上一节中最后示例扩展自定义度量类中编写了MetricsDemo度量类，它扩展了AbuMetricsBase度量类，添加了交易手续费相关度量值，以及可视化方法\n",
    "，下面的示例编写评分类将使用手续费做为一个度量维度对grid结果进行评分，代码如下所示:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuBaseScorer\n",
    "\n",
    "class DemoScorer(AbuBaseScorer):\n",
    "    def _init_self_begin(self, *arg, **kwargs):\n",
    "        \"\"\"胜率，策略收益，手续费组成select_score_func\"\"\"\n",
    "\n",
    "        self.select_score_func = lambda metrics: [metrics.win_rate, metrics.algorithm_period_returns,\n",
    "                                                  metrics.commission_sum]\n",
    "        self.columns_name = ['win_rate', 'returns', 'commission']\n",
    "        self.weights_cnt = len(self.columns_name)\n",
    "\n",
    "    def _init_self_end(self, *arg, **kwargs):\n",
    "        \"\"\"\n",
    "        _init_self_end这里一般的任务是将score_pd中需要反转的反转，默认是数据越大越好，有些是越小越好，\n",
    "        类似make_scorer(xxx, greater_is_better=True)中的参数greater_is_better的作用：\n",
    "\n",
    "                            sign = 1 if greater_is_better else -1\n",
    "        \"\"\"\n",
    "        self.score_pd['commission'] = -self.score_pd['commission']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的代码DemoScorer即实现了自定义评分类：\n",
    "\n",
    "1. 自定义评分类需要继承AbuBaseScorer\n",
    "2. 自定义评分类需要实现_init_self_begin，声明自己要度量metrics中的那些度量值\n",
    "3. 自定义评分类需要实现_init_self_end，在这里将需要反转结果的度量值进行反转\n",
    "\n",
    "本例_init_self_end中使用：\n",
    "\n",
    "    self.score_pd['commission'] = -self.score_pd['commission']\n",
    "    \n",
    "将手续费数值进行反转，因为本例度量的标准是手续费的花销越小代表结果越好，所以需要在_init_self_end中将手续费这一列变成负数。\n",
    "\n",
    "下面构建DemoScorer，注意传递了关键字参数metrics_class，使用MetricsDemo，默认不传递metrics_class将使用AbuMetricsBase做为度量类提供度量值，但是DemoScorer中需要使用metrics.commission_sum，所以这里必须使用MetricsDemo，代码如下所示：\n",
    "\n",
    "备注：已将MetricsDemo做为abupy内置策略示例因子在项目中，所以本节不重复编写，直接从abupy中import，如下所示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win_rate</th>\n",
       "      <th>returns</th>\n",
       "      <th>commission</th>\n",
       "      <th>score_win_rate</th>\n",
       "      <th>score_returns</th>\n",
       "      <th>score_commission</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0339</td>\n",
       "      <td>-1540.17</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>0.9972</td>\n",
       "      <td>0.3592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.3333</td>\n",
       "      <td>-0.1268</td>\n",
       "      <td>-1415.37</td>\n",
       "      <td>0.1538</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.9979</td>\n",
       "      <td>0.3867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>-0.0919</td>\n",
       "      <td>-1284.72</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.0245</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.3485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>-0.0563</td>\n",
       "      <td>-1284.72</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>0.0524</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>0.3578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0821</td>\n",
       "      <td>-1179.97</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.3443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     win_rate  returns  commission  score_win_rate  score_returns  \\\n",
       "248    0.0000  -0.0339    -1540.17          0.0007         0.0797   \n",
       "182    0.3333  -0.1268    -1415.37          0.1538         0.0084   \n",
       "183    0.2000  -0.0919    -1284.72          0.0224         0.0245   \n",
       "184    0.2000  -0.0563    -1284.72          0.0224         0.0524   \n",
       "6      0.0000  -0.0821    -1179.97          0.0007         0.0322   \n",
       "\n",
       "     score_commission   score  \n",
       "248            0.9972  0.3592  \n",
       "182            0.9979  0.3867  \n",
       "183            0.9986  0.3485  \n",
       "184            0.9986  0.3578  \n",
       "6              1.0000  0.3443  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from abupy import MetricsDemo\n",
    "\n",
    "scorer = DemoScorer(score_tuple_array, metrics_class=MetricsDemo)\n",
    "# 返回按照评分排序后的队列\n",
    "scorer_returns_max = scorer.fit_score()\n",
    "scorer.score_pd.sort_values(by='score_commission').tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面代码使用DemoScorer对grid的结果进行评分，从score_pd.tail()中最后一行可以看到commission=-1179.97, 即手续费总开销为1179.97的score_commission评分结果在手续费评分结果中是满分1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win_rate</th>\n",
       "      <th>returns</th>\n",
       "      <th>commission</th>\n",
       "      <th>score_win_rate</th>\n",
       "      <th>score_returns</th>\n",
       "      <th>score_commission</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>0.4478</td>\n",
       "      <td>0.2201</td>\n",
       "      <td>-5008.43</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.8315</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>0.4478</td>\n",
       "      <td>0.2208</td>\n",
       "      <td>-5008.43</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.8322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>0.4478</td>\n",
       "      <td>0.1304</td>\n",
       "      <td>-4975.82</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.5937</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.3727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>0.4478</td>\n",
       "      <td>0.1369</td>\n",
       "      <td>-4975.82</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.6224</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.3823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>0.4478</td>\n",
       "      <td>0.1208</td>\n",
       "      <td>-4975.82</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.5650</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.3632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      win_rate  returns  commission  score_win_rate  score_returns  \\\n",
       "1105    0.4478   0.2201    -5008.43          0.5224         0.8315   \n",
       "1135    0.4478   0.2208    -5008.43          0.5224         0.8322   \n",
       "1075    0.4478   0.1304    -4975.82          0.5224         0.5937   \n",
       "1165    0.4478   0.1369    -4975.82          0.5224         0.6224   \n",
       "1195    0.4478   0.1208    -4975.82          0.5224         0.5650   \n",
       "\n",
       "      score_commission   score  \n",
       "1105            0.0000  0.4513  \n",
       "1135            0.0000  0.4515  \n",
       "1075            0.0021  0.3727  \n",
       "1165            0.0021  0.3823  \n",
       "1195            0.0021  0.3632  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scorer.score_pd.sort_values(by='score_commission').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从score_pd.head()中可以看到手续费开销越大的在手续费分数score_commission这一栏中的得分结果就越低，\n",
    "可想而知如果使用：\n",
    "\n",
    "    scorer = DemoScorer(score_tuple_array, metrics_class=MetricsDemo, weights=[0, 0, 1])\n",
    "       \n",
    "将评分权重都放在手续费的开销上，那么评分的结果最后的即是不怎么进行买入交易，或者买入后不进行卖出交易的参数组合了，读者可自行尝试。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于评分类的更多细节请自行阅读源AbuBaseScorer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小结：对于交易系统的优化，最优参数的选择问题，首先我们要明确所有的参数拟合都是基于历史数据的，即拟合一组最优参数使其对特定历史或者特定一些股票的回测结果趋于完美的实际意义并不大，有时反而适得其反，但是大粒度的统计意义依然具备。比如上面使用的一些因子的参数你设置为100或者其他不靠谱的数那肯定是不合适的，在统计范围内来限制参数的有限个解是有意义的，但是真实的最优参数却是不存在的，不要在最优参数上陷入误区，适可而止，掌握好度，是做好每一件事情的关键。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### abu量化文档目录章节\n",
    "\n",
    "1. [择时策略的开发](http://www.abuquant.com/lecture/lecture_1.html)\n",
    "2. [择时策略的优化](http://www.abuquant.com/lecture/lecture_2.html)\n",
    "3. [滑点策略与交易手续费](http://www.abuquant.com/lecture/lecture_3.html)\n",
    "4. [多支股票择时回测与仓位管理](http://www.abuquant.com/lecture/lecture_4.html)\n",
    "5. [选股策略的开发](http://www.abuquant.com/lecture/lecture_5.html)\n",
    "6. [回测结果的度量](http://www.abuquant.com/lecture/lecture_6.html)\n",
    "7. [寻找策略最优参数和评分](http://www.abuquant.com/lecture/lecture_7.html)\n",
    "8. [A股市场的回测](http://www.abuquant.com/lecture/lecture_8.html)\n",
    "9. [港股市场的回测](http://www.abuquant.com/lecture/lecture_9.html)\n",
    "10. [比特币，莱特币的回测](http://www.abuquant.com/lecture/lecture_10.html)\n",
    "11. [期货市场的回测](http://www.abuquant.com/lecture/lecture_11.html)\n",
    "12. [机器学习与比特币示例](http://www.abuquant.com/lecture/lecture_12.html)\n",
    "13. [量化技术分析应用](http://www.abuquant.com/lecture/lecture_13.html)\n",
    "14. [量化相关性分析应用](http://www.abuquant.com/lecture/lecture_14.html)\n",
    "15. [量化交易和搜索引擎](http://www.abuquant.com/lecture/lecture_15.html)\n",
    "16. [UMP主裁交易决策](http://www.abuquant.com/lecture/lecture_16.html)\n",
    "17. [UMP边裁交易决策](http://www.abuquant.com/lecture/lecture_17.html)\n",
    "18. [自定义裁判决策交易](http://www.abuquant.com/lecture/lecture_18.html)\n",
    "19. [数据源](http://www.abuquant.com/lecture/lecture_19.html)\n",
    "20. [A股全市场回测](http://www.abuquant.com/lecture/lecture_20.html)\n",
    "21. [A股UMP决策](http://www.abuquant.com/lecture/lecture_21.html)\n",
    "22. [美股全市场回测](http://www.abuquant.com/lecture/lecture_22.html)\n",
    "23. [美股UMP决策](http://www.abuquant.com/lecture/lecture_23.html)\n",
    "\n",
    "abu量化系统文档教程持续更新中，请关注公众号中的更新提醒。\n",
    "\n",
    "#### 《量化交易之路》目录章节及随书代码地址\n",
    "\n",
    "1. [第二章 量化语言——Python](https://github.com/bbfamily/abu/tree/master/ipython/第二章-量化语言——Python.ipynb)\n",
    "2. [第三章 量化工具——NumPy](https://github.com/bbfamily/abu/tree/master/ipython/第三章-量化工具——NumPy.ipynb)\n",
    "3. [第四章 量化工具——pandas](https://github.com/bbfamily/abu/tree/master/ipython/第四章-量化工具——pandas.ipynb)\n",
    "4. [第五章 量化工具——可视化](https://github.com/bbfamily/abu/tree/master/ipython/第五章-量化工具——可视化.ipynb)\n",
    "5. [第六章 量化工具——数学：你一生的追求到底能带来多少幸福](https://github.com/bbfamily/abu/tree/master/ipython/第六章-量化工具——数学.ipynb)\n",
    "6. [第七章 量化系统——入门：三只小猪股票投资的故事](https://github.com/bbfamily/abu/tree/master/ipython/第七章-量化系统——入门.ipynb)\n",
    "7. [第八章 量化系统——开发](https://github.com/bbfamily/abu/tree/master/ipython/第八章-量化系统——开发.ipynb)\n",
    "8. [第九章 量化系统——度量与优化](https://github.com/bbfamily/abu/tree/master/ipython/第九章-量化系统——度量与优化.ipynb)\n",
    "9. [第十章 量化系统——机器学习•猪老三](https://github.com/bbfamily/abu/tree/master/ipython/第十章-量化系统——机器学习•猪老三.ipynb)\n",
    "10. [第十一章 量化系统——机器学习•ABU](https://github.com/bbfamily/abu/tree/master/ipython/第十一章-量化系统——机器学习•ABU.ipynb)\n",
    "11. [附录A 量化环境部署](https://github.com/bbfamily/abu/tree/master/ipython/附录A-量化环境部署.ipynb)\n",
    "12. [附录B 量化相关性分析](https://github.com/bbfamily/abu/tree/master/ipython/附录B-量化相关性分析.ipynb)\n",
    "13. [附录C 量化统计分析及指标应用](https://github.com/bbfamily/abu/tree/master/ipython/附录C-量化统计分析及指标应用.ipynb)\n",
    "\n",
    "[更多阿布量化量化技术文章](http://www.abuquant.com/article)\n",
    "\n",
    "更多关于量化交易相关请阅读[《量化交易之路》](http://www.abuquant.com/books/quantify-trading-road.html)\n",
    "\n",
    "更多关于量化交易与机器学习相关请阅读[《机器学习之路》](http://www.abuquant.com/books/machine-learning-road.html)\n",
    "\n",
    "更多关于abu量化系统请关注微信公众号: abu_quant\n",
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    "如有任何问题也可在公众号中联系我的个人微信号。\n",
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    "![](./image/qrcode.jpg)"
   ]
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